######## snakemake preamble start (automatically inserted, do not edit) ########
import sys; sys.path.extend(['/home/ckikawa/.conda/envs/seqneut-pipeline/lib/python3.11/site-packages', '/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/seqneut-pipeline', '/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024', '/home/ckikawa/.conda/envs/seqneut-pipeline/bin', '/home/ckikawa/.conda/envs/seqneut-pipeline/lib/python3.11', '/home/ckikawa/.conda/envs/seqneut-pipeline/lib/python3.11/lib-dynload', '/home/ckikawa/.local/lib/python3.11/site-packages', '/home/ckikawa/.conda/envs/seqneut-pipeline/lib/python3.11/site-packages', '/home/ckikawa/.cache/snakemake/snakemake/source-cache/runtime-cache/tmpy6xj203u/file/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/seqneut-pipeline/notebooks', '/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/seqneut-pipeline/notebooks']); import pickle; snakemake = pickle.loads(b'\x80\x04\x95\xfd\x87\x00\x00\x00\x00\x00\x00\x8c\x10snakemake.script\x94\x8c\tSnakemake\x94\x93\x94)\x81\x94}\x94(\x8c\x05input\x94\x8c\x0csnakemake.io\x94\x8c\nInputFiles\x94\x93\x94)\x81\x94(\x8c(results/barcode_counts/plate2_none-1.csv\x94\x8c(results/barcode_counts/plate2_none-2.csv\x94\x8c(results/barcode_counts/plate2_none-3.csv\x94\x8c(results/barcode_counts/plate2_none-4.csv\x94\x8c5results/barcode_counts/plate2_A230212d0_r16_100.0.csv\x94\x8c5results/barcode_counts/plate2_A230212d0_r16_150.0.csv\x94\x8c5results/barcode_counts/plate2_A230212d0_r16_225.0.csv\x94\x8c5results/barcode_counts/plate2_A230212d0_r16_337.5.csv\x94\x8c6results/barcode_counts/plate2_A230212d0_r16_506.25.csv\x94\x8c7results/barcode_counts/plate2_A230212d0_r16_759.375.csv\x94\x8c9results/barcode_counts/plate2_A230212d0_r16_1139.0625.csv\x94\x8c:results/barcode_counts/plate2_A230212d0_r16_1708.59375.csv\x94\x8c;results/barcode_counts/plate2_A230212d0_r16_2562.890625.csv\x94\x8c;results/barcode_counts/plate2_A230212d0_r16_3844.335938.csv\x94\x8c;results/barcode_counts/plate2_A230212d0_r16_5766.503906.csv\x94\x8c;results/barcode_counts/plate2_A230212d0_r16_8649.755859.csv\x94\x8c6results/barcode_counts/plate2_A230212d28_r16_100.0.csv\x94\x8c6results/barcode_counts/plate2_A230212d28_r16_150.0.csv\x94\x8c6results/barcode_counts/plate2_A230212d28_r16_225.0.csv\x94\x8c6results/barcode_counts/plate2_A230212d28_r16_337.5.csv\x94\x8c7results/barcode_counts/plate2_A230212d28_r16_506.25.csv\x94\x8c8results/barcode_counts/plate2_A230212d28_r16_759.375.csv\x94\x8c:results/barcode_counts/plate2_A230212d28_r16_1139.0625.csv\x94\x8c;results/barcode_counts/plate2_A230212d28_r16_1708.59375.csv\x94\x8c<results/barcode_counts/plate2_A230212d28_r16_2562.890625.csv\x94\x8c<results/barcode_counts/plate2_A230212d28_r16_3844.335938.csv\x94\x8c<results/barcode_counts/plate2_A230212d28_r16_5766.503906.csv\x94\x8c<results/barcode_counts/plate2_A230212d28_r16_8649.755859.csv\x94\x8c\'results/barcode_fates/plate2_none-1.csv\x94\x8c\'results/barcode_fates/plate2_none-2.csv\x94\x8c\'results/barcode_fates/plate2_none-3.csv\x94\x8c\'results/barcode_fates/plate2_none-4.csv\x94\x8c4results/barcode_fates/plate2_A230212d0_r16_100.0.csv\x94\x8c4results/barcode_fates/plate2_A230212d0_r16_150.0.csv\x94\x8c4results/barcode_fates/plate2_A230212d0_r16_225.0.csv\x94\x8c4results/barcode_fates/plate2_A230212d0_r16_337.5.csv\x94\x8c5results/barcode_fates/plate2_A230212d0_r16_506.25.csv\x94\x8c6results/barcode_fates/plate2_A230212d0_r16_759.375.csv\x94\x8c8results/barcode_fates/plate2_A230212d0_r16_1139.0625.csv\x94\x8c9results/barcode_fates/plate2_A230212d0_r16_1708.59375.csv\x94\x8c:results/barcode_fates/plate2_A230212d0_r16_2562.890625.csv\x94\x8c:results/barcode_fates/plate2_A230212d0_r16_3844.335938.csv\x94\x8c:results/barcode_fates/plate2_A230212d0_r16_5766.503906.csv\x94\x8c:results/barcode_fates/plate2_A230212d0_r16_8649.755859.csv\x94\x8c5results/barcode_fates/plate2_A230212d28_r16_100.0.csv\x94\x8c5results/barcode_fates/plate2_A230212d28_r16_150.0.csv\x94\x8c5results/barcode_fates/plate2_A230212d28_r16_225.0.csv\x94\x8c5results/barcode_fates/plate2_A230212d28_r16_337.5.csv\x94\x8c6results/barcode_fates/plate2_A230212d28_r16_506.25.csv\x94\x8c7results/barcode_fates/plate2_A230212d28_r16_759.375.csv\x94\x8c9results/barcode_fates/plate2_A230212d28_r16_1139.0625.csv\x94\x8c:results/barcode_fates/plate2_A230212d28_r16_1708.59375.csv\x94\x8c;results/barcode_fates/plate2_A230212d28_r16_2562.890625.csv\x94\x8c;results/barcode_fates/plate2_A230212d28_r16_3844.335938.csv\x94\x8c;results/barcode_fates/plate2_A230212d28_r16_5766.503906.csv\x94\x8c;results/barcode_fates/plate2_A230212d28_r16_8649.755859.csv\x94\x8c)data/viral_libraries/2023_H3N2_Kikawa.csv\x94\x8c3data/neut_standard_sets/loes2023_neut_standards.csv\x94e}\x94(\x8c\x06_names\x94}\x94(\x8c\ncount_csvs\x94K\x00K\x1c\x86\x94\x8c\tfate_csvs\x94K\x1cK8\x86\x94\x8c\x11viral_library_csv\x94K8N\x86\x94\x8c\x15neut_standard_set_csv\x94K9N\x86\x94u\x8c\x12_allowed_overrides\x94]\x94(\x8c\x05index\x94\x8c\x04sort\x94ehQ\x8c\tfunctools\x94\x8c\x07partial\x94\x93\x94h\x06\x8c\x19Namedlist._used_attribute\x94\x93\x94\x85\x94R\x94(hW)}\x94\x8c\x05_name\x94hQsNt\x94bhRhUhW\x85\x94R\x94(hW)}\x94h[hRsNt\x94bhGh\x06\x8c\tNamedlist\x94\x93\x94)\x81\x94(h\nh\x0bh\x0ch\rh\x0eh\x0fh\x10h\x11h\x12h\x13h\x14h\x15h\x16h\x17h\x18h\x19h\x1ah\x1bh\x1ch\x1dh\x1eh\x1fh h!h"h#h$h%e}\x94(hE}\x94hO]\x94(hQhRehQhUhW\x85\x94R\x94(hW)}\x94h[hQsNt\x94bhRhUhW\x85\x94R\x94(hW)}\x94h[hRsNt\x94bubhIhb)\x81\x94(h&h\'h(h)h*h+h,h-h.h/h0h1h2h3h4h5h6h7h8h9h:h;h<h=h>h?h@hAe}\x94(hE}\x94hO]\x94(hQhRehQhUhW\x85\x94R\x94(hW)}\x94h[hQsNt\x94bhRhUhW\x85\x94R\x94(hW)}\x94h[hRsNt\x94bubhKhBhMhCub\x8c\x06output\x94h\x06\x8c\x0bOutputFiles\x94\x93\x94)\x81\x94(\x8c"results/plates/plate2/qc_drops.yml\x94\x8c*results/plates/plate2/frac_infectivity.csv\x94\x8c#results/plates/plate2/curvefits.csv\x94\x8c&results/plates/plate2/curvefits.pickle\x94e}\x94(hE}\x94(\x8c\x08qc_drops\x94K\x00N\x86\x94\x8c\x14frac_infectivity_csv\x94K\x01N\x86\x94\x8c\x08fits_csv\x94K\x02N\x86\x94\x8c\x0bfits_pickle\x94K\x03N\x86\x94uhO]\x94(hQhRehQhUhW\x85\x94R\x94(hW)}\x94h[hQsNt\x94bhRhUhW\x85\x94R\x94(hW)}\x94h[hRsNt\x94bh\x85h\x7fh\x87h\x80h\x89h\x81h\x8bh\x82ub\x8c\x06params\x94h\x06\x8c\x06Params\x94\x93\x94)\x81\x94(]\x94(\x8c\rplate2_none-1\x94\x8c\rplate2_none-2\x94\x8c\rplate2_none-3\x94\x8c\rplate2_none-4\x94\x8c\x1aplate2_A230212d0_r16_100.0\x94\x8c\x1aplate2_A230212d0_r16_150.0\x94\x8c\x1aplate2_A230212d0_r16_225.0\x94\x8c\x1aplate2_A230212d0_r16_337.5\x94\x8c\x1bplate2_A230212d0_r16_506.25\x94\x8c\x1cplate2_A230212d0_r16_759.375\x94\x8c\x1eplate2_A230212d0_r16_1139.0625\x94\x8c\x1fplate2_A230212d0_r16_1708.59375\x94\x8c plate2_A230212d0_r16_2562.890625\x94\x8c plate2_A230212d0_r16_3844.335938\x94\x8c plate2_A230212d0_r16_5766.503906\x94\x8c plate2_A230212d0_r16_8649.755859\x94\x8c\x1bplate2_A230212d28_r16_100.0\x94\x8c\x1bplate2_A230212d28_r16_150.0\x94\x8c\x1bplate2_A230212d28_r16_225.0\x94\x8c\x1bplate2_A230212d28_r16_337.5\x94\x8c\x1cplate2_A230212d28_r16_506.25\x94\x8c\x1dplate2_A230212d28_r16_759.375\x94\x8c\x1fplate2_A230212d28_r16_1139.0625\x94\x8c plate2_A230212d28_r16_1708.59375\x94\x8c!plate2_A230212d28_r16_2562.890625\x94\x8c!plate2_A230212d28_r16_3844.335938\x94\x8c!plate2_A230212d28_r16_5766.503906\x94\x8c!plate2_A230212d28_r16_8649.755859\x94e}\x94(\x8c\x05group\x94\x8c\x05pilot\x94\x8c\x04date\x94\x8c\n2024-01-25\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c.data/plates/2024-01-22_plate_mapping_file1.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(\x8c\x1bavg_barcode_counts_per_well\x94M\xf4\x01\x8c\x1fmin_neut_standard_frac_per_well\x94G?tz\xe1G\xae\x14{\x8c"no_serum_per_viral_barcode_filters\x94}\x94(\x8c\x08min_frac\x94G?\x1a6\xe2\xeb\x1cC-\x8c\x0fmax_fold_change\x94K\x04\x8c\tmax_wells\x94K\x02u\x8c!per_neut_standard_barcode_filters\x94}\x94(\x8c\x08min_frac\x94G?tz\xe1G\xae\x14{\x8c\x0fmax_fold_change\x94K\x04\x8c\tmax_wells\x94K\x02u\x8c min_neut_standard_count_per_well\x94M\xe8\x03\x8c)min_no_serum_count_per_viral_barcode_well\x94Kd\x8c+max_frac_infectivity_per_viral_barcode_well\x94K\x03\x8c)min_dilutions_per_barcode_serum_replicate\x94K\x06u\x8c\x0fcurvefit_params\x94}\x94(\x8c\x18frac_infectivity_ceiling\x94K\x01\x8c\x06fixtop\x94]\x94(G?\xe3333333K\x01e\x8c\tfixbottom\x94K\x00\x8c\x08fixslope\x94]\x94(G?\xe9\x99\x99\x99\x99\x99\x9aK\neu\x8c\x0bcurvefit_qc\x94}\x94(\x8c\x1dmax_frac_infectivity_at_least\x94G\x00\x00\x00\x00\x00\x00\x00\x00\x8c\x0fgoodness_of_fit\x94}\x94(\x8c\x06min_R2\x94G?\xe0\x00\x00\x00\x00\x00\x00\x8c\x08max_RMSD\x94G?\xc3333333u\x8c#serum_replicates_ignore_curvefit_qc\x94]\x94\x8c+barcode_serum_replicates_ignore_curvefit_qc\x94]\x94u\x8c\x1eillumina_barcode_parser_params\x94}\x94(\x8c\x08upstream\x94\x8c\x1cCCTACAATGTCGGATTTGTATTTAATAG\x94\x8c\ndownstream\x94\x8c\x00\x94\x8c\x04minq\x94K\x14\x8c\x11upstream_mismatch\x94K\x04\x8c\x0ebc_orientation\x94\x8c\x02R2\x94u\x8c\x07samples\x94}\x94(\x8c\x04well\x94}\x94(K\x00\x8c\x02B5\x94K\x01\x8c\x02B6\x94K\x02\x8c\x02B7\x94K\x03\x8c\x02B8\x94K\x04\x8c\x02D1\x94K\x05\x8c\x02D2\x94K\x06\x8c\x02D3\x94K\x07\x8c\x02D4\x94K\x08\x8c\x02D5\x94K\t\x8c\x02D6\x94K\n\x8c\x02D7\x94K\x0b\x8c\x02D8\x94K\x0c\x8c\x02D9\x94K\r\x8c\x03D10\x94K\x0e\x8c\x03D11\x94K\x0f\x8c\x03D12\x94K\x10\x8c\x02G1\x94K\x11\x8c\x02G2\x94K\x12\x8c\x02G3\x94K\x13\x8c\x02G4\x94K\x14\x8c\x02G5\x94K\x15\x8c\x02G6\x94K\x16\x8c\x02G7\x94K\x17\x8c\x02G8\x94K\x18\x8c\x02G9\x94K\x19\x8c\x03G10\x94K\x1a\x8c\x03G11\x94K\x1b\x8c\x03G12\x94u\x8c\x05serum\x94}\x94(K\x00\x8c\x04none\x94K\x01j\x15\x01\x00\x00K\x02j\x15\x01\x00\x00K\x03j\x15\x01\x00\x00K\x04\x8c\rA230212d0_r16\x94K\x05j\x16\x01\x00\x00K\x06j\x16\x01\x00\x00K\x07j\x16\x01\x00\x00K\x08j\x16\x01\x00\x00K\tj\x16\x01\x00\x00K\nj\x16\x01\x00\x00K\x0bj\x16\x01\x00\x00K\x0cj\x16\x01\x00\x00K\rj\x16\x01\x00\x00K\x0ej\x16\x01\x00\x00K\x0fj\x16\x01\x00\x00K\x10\x8c\x0eA230212d28_r16\x94K\x11j\x17\x01\x00\x00K\x12j\x17\x01\x00\x00K\x13j\x17\x01\x00\x00K\x14j\x17\x01\x00\x00K\x15j\x17\x01\x00\x00K\x16j\x17\x01\x00\x00K\x17j\x17\x01\x00\x00K\x18j\x17\x01\x00\x00K\x19j\x17\x01\x00\x00K\x1aj\x17\x01\x00\x00K\x1bj\x17\x01\x00\x00u\x8c\x0fdilution_factor\x94}\x94(K\x00G\x7f\xf8\x00\x00\x00\x00\x00\x00K\x01G\x7f\xf8\x00\x00\x00\x00\x00\x00K\x02G\x7f\xf8\x00\x00\x00\x00\x00\x00K\x03G\x7f\xf8\x00\x00\x00\x00\x00\x00K\x04G@Y\x00\x00\x00\x00\x00\x00K\x05G@b\xc0\x00\x00\x00\x00\x00K\x06G@l \x00\x00\x00\x00\x00K\x07G@u\x18\x00\x00\x00\x00\x00K\x08G@\x7f\xa4\x00\x00\x00\x00\x00K\tG@\x87\xbb\x00\x00\x00\x00\x00K\nG@\x91\xcc@\x00\x00\x00\x00K\x0bG@\x9a\xb2`\x00\x00\x00\x00K\x0cG@\xa4\x05\xc8\x00\x00\x00\x00K\rG@\xae\x08\xac\x00\x10\xc6\xf8K\x0eG@\xb6\x86\x80\xff\xfb\xceBK\x0fG@\xc0\xe4\xe0\xbf\xfc\xda\xb2K\x10G@Y\x00\x00\x00\x00\x00\x00K\x11G@b\xc0\x00\x00\x00\x00\x00K\x12G@l \x00\x00\x00\x00\x00K\x13G@u\x18\x00\x00\x00\x00\x00K\x14G@\x7f\xa4\x00\x00\x00\x00\x00K\x15G@\x87\xbb\x00\x00\x00\x00\x00K\x16G@\x91\xcc@\x00\x00\x00\x00K\x17G@\x9a\xb2`\x00\x00\x00\x00K\x18G@\xa4\x05\xc8\x00\x00\x00\x00K\x19G@\xae\x08\xac\x00\x10\xc6\xf8K\x1aG@\xb6\x86\x80\xff\xfb\xceBK\x1bG@\xc0\xe4\xe0\xbf\xfc\xda\xb2u\x8c\treplicate\x94}\x94(K\x00K\x01K\x01K\x02K\x02K\x03K\x03K\x04K\x04K\x01K\x05K\x01K\x06K\x01K\x07K\x01K\x08K\x01K\tK\x01K\nK\x01K\x0bK\x01K\x0cK\x01K\rK\x01K\x0eK\x01K\x0fK\x01K\x10K\x01K\x11K\x01K\x12K\x01K\x13K\x01K\x14K\x01K\x15K\x01K\x16K\x01K\x17K\x01K\x18K\x01K\x19K\x01K\x1aK\x01K\x1bK\x01u\x8c\x05fastq\x94}\x94(K\x00\x8c\x85/fh/fast/bloom_j/SR/ngs/illumina/ckikawa/240124_VH01189_225_AAFGG2HM5/Unaligned/Project_ckikawa/rd16_rep1_noSerum_S39_R1_001.fastq.gz\x94K\x01\x8c\x85/fh/fast/bloom_j/SR/ngs/illumina/ckikawa/240124_VH01189_225_AAFGG2HM5/Unaligned/Project_ckikawa/rd16_rep2_noSerum_S47_R1_001.fastq.gz\x94K\x02\x8c\x85/fh/fast/bloom_j/SR/ngs/illumina/ckikawa/240124_VH01189_225_AAFGG2HM5/Unaligned/Project_ckikawa/rd16_rep3_noSerum_S55_R1_001.fastq.gz\x94K\x03\x8c\x85/fh/fast/bloom_j/SR/ngs/illumina/ckikawa/240124_VH01189_225_AAFGG2HM5/Unaligned/Project_ckikawa/rd16_rep4_noSerum_S63_R1_001.fastq.gz\x94K\x04\x8c\x89/fh/fast/bloom_j/SR/ngs/illumina/ckikawa/240124_VH01189_225_AAFGG2HM5/Unaligned/Project_ckikawa/rd16_A23_0212_d0_conc1_S5_R1_001.fastq.gz\x94K\x05\x8c\x8a/fh/fast/bloom_j/SR/ngs/illumina/ckikawa/240124_VH01189_225_AAFGG2HM5/Unaligned/Project_ckikawa/rd16_A23_0212_d0_conc2_S13_R1_001.fastq.gz\x94K\x06\x8c\x8a/fh/fast/bloom_j/SR/ngs/illumina/ckikawa/240124_VH01189_225_AAFGG2HM5/Unaligned/Project_ckikawa/rd16_A23_0212_d0_conc3_S21_R1_001.fastq.gz\x94K\x07\x8c\x8a/fh/fast/bloom_j/SR/ngs/illumina/ckikawa/240124_VH01189_225_AAFGG2HM5/Unaligned/Project_ckikawa/rd16_A23_0212_d0_conc4_S29_R1_001.fastq.gz\x94K\x08\x8c\x8a/fh/fast/bloom_j/SR/ngs/illumina/ckikawa/240124_VH01189_225_AAFGG2HM5/Unaligned/Project_ckikawa/rd16_A23_0212_d0_conc5_S37_R1_001.fastq.gz\x94K\t\x8c\x8a/fh/fast/bloom_j/SR/ngs/illumina/ckikawa/240124_VH01189_225_AAFGG2HM5/Unaligned/Project_ckikawa/rd16_A23_0212_d0_conc6_S45_R1_001.fastq.gz\x94K\n\x8c\x8a/fh/fast/bloom_j/SR/ngs/illumina/ckikawa/240124_VH01189_225_AAFGG2HM5/Unaligned/Project_ckikawa/rd16_A23_0212_d0_conc7_S53_R1_001.fastq.gz\x94K\x0b\x8c\x8a/fh/fast/bloom_j/SR/ngs/illumina/ckikawa/240124_VH01189_225_AAFGG2HM5/Unaligned/Project_ckikawa/rd16_A23_0212_d0_conc8_S61_R1_001.fastq.gz\x94K\x0c\x8c\x8a/fh/fast/bloom_j/SR/ngs/illumina/ckikawa/240124_VH01189_225_AAFGG2HM5/Unaligned/Project_ckikawa/rd16_A23_0212_d0_conc9_S69_R1_001.fastq.gz\x94K\r\x8c\x8b/fh/fast/bloom_j/SR/ngs/illumina/ckikawa/240124_VH01189_225_AAFGG2HM5/Unaligned/Project_ckikawa/rd16_A23_0212_d0_conc10_S77_R1_001.fastq.gz\x94K\x0e\x8c\x8b/fh/fast/bloom_j/SR/ngs/illumina/ckikawa/240124_VH01189_225_AAFGG2HM5/Unaligned/Project_ckikawa/rd16_A23_0212_d0_conc11_S85_R1_001.fastq.gz\x94K\x0f\x8c\x8b/fh/fast/bloom_j/SR/ngs/illumina/ckikawa/240124_VH01189_225_AAFGG2HM5/Unaligned/Project_ckikawa/rd16_A23_0212_d0_conc12_S93_R1_001.fastq.gz\x94K\x10\x8c\x8a/fh/fast/bloom_j/SR/ngs/illumina/ckikawa/240124_VH01189_225_AAFGG2HM5/Unaligned/Project_ckikawa/rd16_A23_0212_d28_conc1_S2_R1_001.fastq.gz\x94K\x11\x8c\x8b/fh/fast/bloom_j/SR/ngs/illumina/ckikawa/240124_VH01189_225_AAFGG2HM5/Unaligned/Project_ckikawa/rd16_A23_0212_d28_conc2_S10_R1_001.fastq.gz\x94K\x12\x8c\x8b/fh/fast/bloom_j/SR/ngs/illumina/ckikawa/240124_VH01189_225_AAFGG2HM5/Unaligned/Project_ckikawa/rd16_A23_0212_d28_conc3_S18_R1_001.fastq.gz\x94K\x13\x8c\x8b/fh/fast/bloom_j/SR/ngs/illumina/ckikawa/240124_VH01189_225_AAFGG2HM5/Unaligned/Project_ckikawa/rd16_A23_0212_d28_conc4_S26_R1_001.fastq.gz\x94K\x14\x8c\x8b/fh/fast/bloom_j/SR/ngs/illumina/ckikawa/240124_VH01189_225_AAFGG2HM5/Unaligned/Project_ckikawa/rd16_A23_0212_d28_conc5_S34_R1_001.fastq.gz\x94K\x15\x8c\x8b/fh/fast/bloom_j/SR/ngs/illumina/ckikawa/240124_VH01189_225_AAFGG2HM5/Unaligned/Project_ckikawa/rd16_A23_0212_d28_conc6_S42_R1_001.fastq.gz\x94K\x16\x8c\x8b/fh/fast/bloom_j/SR/ngs/illumina/ckikawa/240124_VH01189_225_AAFGG2HM5/Unaligned/Project_ckikawa/rd16_A23_0212_d28_conc7_S50_R1_001.fastq.gz\x94K\x17\x8c\x8b/fh/fast/bloom_j/SR/ngs/illumina/ckikawa/240124_VH01189_225_AAFGG2HM5/Unaligned/Project_ckikawa/rd16_A23_0212_d28_conc8_S58_R1_001.fastq.gz\x94K\x18\x8c\x8b/fh/fast/bloom_j/SR/ngs/illumina/ckikawa/240124_VH01189_225_AAFGG2HM5/Unaligned/Project_ckikawa/rd16_A23_0212_d28_conc9_S66_R1_001.fastq.gz\x94K\x19\x8c\x8c/fh/fast/bloom_j/SR/ngs/illumina/ckikawa/240124_VH01189_225_AAFGG2HM5/Unaligned/Project_ckikawa/rd16_A23_0212_d28_conc10_S74_R1_001.fastq.gz\x94K\x1a\x8c\x8c/fh/fast/bloom_j/SR/ngs/illumina/ckikawa/240124_VH01189_225_AAFGG2HM5/Unaligned/Project_ckikawa/rd16_A23_0212_d28_conc11_S82_R1_001.fastq.gz\x94K\x1b\x8c\x8c/fh/fast/bloom_j/SR/ngs/illumina/ckikawa/240124_VH01189_225_AAFGG2HM5/Unaligned/Project_ckikawa/rd16_A23_0212_d28_conc12_S90_R1_001.fastq.gz\x94u\x8c\x0fserum_replicate\x94}\x94(K\x00\x8c\x06none-1\x94K\x01\x8c\x06none-2\x94K\x02\x8c\x06none-3\x94K\x03\x8c\x06none-4\x94K\x04j\x16\x01\x00\x00K\x05j\x16\x01\x00\x00K\x06j\x16\x01\x00\x00K\x07j\x16\x01\x00\x00K\x08j\x16\x01\x00\x00K\tj\x16\x01\x00\x00K\nj\x16\x01\x00\x00K\x0bj\x16\x01\x00\x00K\x0cj\x16\x01\x00\x00K\rj\x16\x01\x00\x00K\x0ej\x16\x01\x00\x00K\x0fj\x16\x01\x00\x00K\x10j\x17\x01\x00\x00K\x11j\x17\x01\x00\x00K\x12j\x17\x01\x00\x00K\x13j\x17\x01\x00\x00K\x14j\x17\x01\x00\x00K\x15j\x17\x01\x00\x00K\x16j\x17\x01\x00\x00K\x17j\x17\x01\x00\x00K\x18j\x17\x01\x00\x00K\x19j\x17\x01\x00\x00K\x1aj\x17\x01\x00\x00K\x1bj\x17\x01\x00\x00u\x8c\x0esample_noplate\x94}\x94(K\x00j<\x01\x00\x00K\x01j=\x01\x00\x00K\x02j>\x01\x00\x00K\x03j?\x01\x00\x00K\x04\x8c\x13A230212d0_r16_100.0\x94K\x05\x8c\x13A230212d0_r16_150.0\x94K\x06\x8c\x13A230212d0_r16_225.0\x94K\x07\x8c\x13A230212d0_r16_337.5\x94K\x08\x8c\x14A230212d0_r16_506.25\x94K\t\x8c\x15A230212d0_r16_759.375\x94K\n\x8c\x17A230212d0_r16_1139.0625\x94K\x0b\x8c\x18A230212d0_r16_1708.59375\x94K\x0c\x8c\x19A230212d0_r16_2562.890625\x94K\r\x8c\x19A230212d0_r16_3844.335938\x94K\x0e\x8c\x19A230212d0_r16_5766.503906\x94K\x0f\x8c\x19A230212d0_r16_8649.755859\x94K\x10\x8c\x14A230212d28_r16_100.0\x94K\x11\x8c\x14A230212d28_r16_150.0\x94K\x12\x8c\x14A230212d28_r16_225.0\x94K\x13\x8c\x14A230212d28_r16_337.5\x94K\x14\x8c\x15A230212d28_r16_506.25\x94K\x15\x8c\x16A230212d28_r16_759.375\x94K\x16\x8c\x18A230212d28_r16_1139.0625\x94K\x17\x8c\x19A230212d28_r16_1708.59375\x94K\x18\x8c\x1aA230212d28_r16_2562.890625\x94K\x19\x8c\x1aA230212d28_r16_3844.335938\x94K\x1a\x8c\x1aA230212d28_r16_5766.503906\x94K\x1b\x8c\x1aA230212d28_r16_8649.755859\x94u\x8c\x06sample\x94}\x94(K\x00h\x9bK\x01h\x9cK\x02h\x9dK\x03h\x9eK\x04h\x9fK\x05h\xa0K\x06h\xa1K\x07h\xa2K\x08h\xa3K\th\xa4K\nh\xa5K\x0bh\xa6K\x0ch\xa7K\rh\xa8K\x0eh\xa9K\x0fh\xaaK\x10h\xabK\x11h\xacK\x12h\xadK\x13h\xaeK\x14h\xafK\x15h\xb0K\x16h\xb1K\x17h\xb2K\x18h\xb3K\x19h\xb4K\x1ah\xb5K\x1bh\xb6u\x8c\x05plate\x94}\x94(K\x00\x8c\x06plate2\x94K\x01j^\x01\x00\x00K\x02j^\x01\x00\x00K\x03j^\x01\x00\x00K\x04j^\x01\x00\x00K\x05j^\x01\x00\x00K\x06j^\x01\x00\x00K\x07j^\x01\x00\x00K\x08j^\x01\x00\x00K\tj^\x01\x00\x00K\nj^\x01\x00\x00K\x0bj^\x01\x00\x00K\x0cj^\x01\x00\x00K\rj^\x01\x00\x00K\x0ej^\x01\x00\x00K\x0fj^\x01\x00\x00K\x10j^\x01\x00\x00K\x11j^\x01\x00\x00K\x12j^\x01\x00\x00K\x13j^\x01\x00\x00K\x14j^\x01\x00\x00K\x15j^\x01\x00\x00K\x16j^\x01\x00\x00K\x17j^\x01\x00\x00K\x18j^\x01\x00\x00K\x19j^\x01\x00\x00K\x1aj^\x01\x00\x00K\x1bj^\x01\x00\x00u\x8c\x0fplate_replicate\x94}\x94(K\x00\x8c\x08plate2-1\x94K\x01\x8c\x08plate2-2\x94K\x02\x8c\x08plate2-3\x94K\x03\x8c\x08plate2-4\x94K\x04j^\x01\x00\x00K\x05j^\x01\x00\x00K\x06j^\x01\x00\x00K\x07j^\x01\x00\x00K\x08j^\x01\x00\x00K\tj^\x01\x00\x00K\nj^\x01\x00\x00K\x0bj^\x01\x00\x00K\x0cj^\x01\x00\x00K\rj^\x01\x00\x00K\x0ej^\x01\x00\x00K\x0fj^\x01\x00\x00K\x10j^\x01\x00\x00K\x11j^\x01\x00\x00K\x12j^\x01\x00\x00K\x13j^\x01\x00\x00K\x14j^\x01\x00\x00K\x15j^\x01\x00\x00K\x16j^\x01\x00\x00K\x17j^\x01\x00\x00K\x18j^\x01\x00\x00K\x19j^\x01\x00\x00K\x1aj^\x01\x00\x00K\x1bj^\x01\x00\x00uuue}\x94(hE}\x94(h\xf3K\x00N\x86\x94\x8c\x0cplate_params\x94K\x01N\x86\x94uhO]\x94(hQhRehQhUhW\x85\x94R\x94(hW)}\x94h[hQsNt\x94bhRhUhW\x85\x94R\x94(hW)}\x94h[hRsNt\x94bh\xf3h\x9ajh\x01\x00\x00h\xb7ub\x8c\twildcards\x94h\x06\x8c\tWildcards\x94\x93\x94)\x81\x94\x8c\x06plate2\x94a}\x94(hE}\x94\x8c\x05plate\x94K\x00N\x86\x94shO]\x94(hQhRehQhUhW\x85\x94R\x94(hW)}\x94h[hQsNt\x94bhRhUhW\x85\x94R\x94(hW)}\x94h[hRsNt\x94bj\\\x01\x00\x00jw\x01\x00\x00ub\x8c\x07threads\x94K\x01\x8c\tresources\x94h\x06\x8c\tResources\x94\x93\x94)\x81\x94(K\x01K\x01\x8c\x15/loc/scratch/64245082\x94e}\x94(hE}\x94(\x8c\x06_cores\x94K\x00N\x86\x94\x8c\x06_nodes\x94K\x01N\x86\x94\x8c\x06tmpdir\x94K\x02N\x86\x94uhO]\x94(hQhRehQhUhW\x85\x94R\x94(hW)}\x94h[hQsNt\x94bhRhUhW\x85\x94R\x94(hW)}\x94h[hRsNt\x94bj\x8d\x01\x00\x00K\x01j\x8f\x01\x00\x00K\x01j\x91\x01\x00\x00j\x8a\x01\x00\x00ub\x8c\x03log\x94h\x06\x8c\x03Log\x94\x93\x94)\x81\x94\x8c*results/plates/plate2/process_plate2.ipynb\x94a}\x94(hE}\x94\x8c\x08notebook\x94K\x00N\x86\x94shO]\x94(hQhRehQhUhW\x85\x94R\x94(hW)}\x94h[hQsNt\x94bhRhUhW\x85\x94R\x94(hW)}\x94h[hRsNt\x94bj\xa3\x01\x00\x00j\xa0\x01\x00\x00ub\x8c\x06config\x94}\x94(\x8c\x10seqneut-pipeline\x94\x8c\x10seqneut-pipeline\x94\x8c\x04docs\x94\x8c\x04docs\x94\x8c\x0bdescription\x94X\n\x01\x00\x00# Sequencing-based neutralization assays of 2023-2024 human serum samples versus H3N2 influenza libraries\n\nThe numerical data and computer code are at [https://github.com/jbloomlab/flu_seqneut_H3N2_2023-2024](https://github.com/jbloomlab/flu_seqneut_H3N2_2023-2024)\n\x94\x8c\x0fviral_libraries\x94}\x94(\x8c\x0cH3N2_library\x94\x8c)data/viral_libraries/2023_H3N2_Kikawa.csv\x94\x8c\x0cH1N1_library\x94\x8c-data/viral_libraries/pdmH1N1_lib2023_loes.csv\x94u\x8c\x17viral_strain_plot_order\x94\x8c)data/H3N2library_2023-2024_allStrains.csv\x94\x8c\x12neut_standard_sets\x94}\x94\x8c\x08loes2023\x94\x8c3data/neut_standard_sets/loes2023_neut_standards.csv\x94s\x8c\x1eillumina_barcode_parser_params\x94}\x94(h\xebh\xech\xedh\xeeh\xefK\x14h\xf0K\x04h\xf1h\xf2u\x8c#default_process_plate_qc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c%default_process_plate_curvefit_params\x94}\x94(h\xd8K\x01h\xd9]\x94(G?\xe3333333K\x01eh\xdbK\x00h\xdc]\x94(G?\xe9\x99\x99\x99\x99\x99\x9aK\neu\x8c!default_process_plate_curvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5]\x94h\xe7]\x94u\x8c\x16default_serum_titer_as\x94\x8c\x08midpoint\x94\x8c\x1bdefault_serum_qc_thresholds\x94}\x94(\x8c\x0emin_replicates\x94K\x02\x8c\x1bmax_fold_change_from_median\x94K\x06\x8c\x11viruses_ignore_qc\x94]\x94u\x8c\x16sera_override_defaults\x94}\x94\x8c\x06plates\x94}\x94(\x8c\x06plate1\x94}\x94(\x8c\x05group\x94\x8c\x05pilot\x94\x8c\x04date\x94\x8c\x08datetime\x94\x8c\x04date\x94\x93\x94C\x04\x07\xe8\x01\x19\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c.data/plates/2024-01-22_plate_mapping_file2.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00uuj^\x01\x00\x00}\x94(h\xb8h\xb9h\xbaj\xe4\x01\x00\x00C\x04\x07\xe8\x01\x19\x94\x85\x94R\x94h\xbch\xbdh\xbeh\xbfh\xc0h\xc1h\xc2}\x94h\xc4}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06uh\xd6}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00uh\xde}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00uu\x8c\x06plate3\x94}\x94(\x8c\x05group\x94\x8c\x05pilot\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x01\x19\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c.data/plates/2024-01-22_plate_mapping_file3.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\x18barcode_serum_replicates\x94]\x94(]\x94(\x8c\x10AATGACAGCTGTCTAG\x94\x8c\x12A230212d0_r64_rep1\x94e]\x94(\x8c\x10ATAGAAAATTATCCGC\x94\x8c\x12A230212d0_r64_rep1\x94ees\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00uu\x8c\x06plate4\x94}\x94(\x8c\x05group\x94\x8c\x05pilot\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x02\x07\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c.data/plates/2024-02-07_plate_mapping_file7.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00uu\x8c\x06plate5\x94}\x94(\x8c\x05group\x94\x8c\x05pilot\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x02\x07\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c.data/plates/2024-02-07_plate_mapping_file4.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00uu\x8c\x06plate6\x94}\x94(\x8c\x05group\x94\x8c\x05pilot\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x02\x07\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c.data/plates/2024-02-07_plate_mapping_file5.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00uu\x8c\x06plate7\x94}\x94(\x8c\x05group\x94\x8c\x05pilot\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x02\x07\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c.data/plates/2024-02-07_plate_mapping_file6.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00uu\x8c\x0eplate8_r32_50k\x94}\x94(\x8c\x05group\x94\x8c\x05pilot\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x03\x04\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c/data/plates/2024-03-04_plate_mapping_file10.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00uu\x8c\x0eplate9_r64_50k\x94}\x94(\x8c\x05group\x94\x8c\x05pilot\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x03\x04\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c/data/plates/2024-03-04_plate_mapping_file12.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00uu\x8c\x10plate10_r128_50k\x94}\x94(\x8c\x05group\x94\x8c\x05pilot\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x03\x04\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c.data/plates/2024-03-04_plate_mapping_file8.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00uu\x8c\x0fplate11_r32_75k\x94}\x94(\x8c\x05group\x94\x8c\x05pilot\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x03\x04\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c/data/plates/2024-03-04_plate_mapping_file11.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00uu\x8c\x0fplate12_r64_75k\x94}\x94(\x8c\x05group\x94\x8c\x05pilot\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x03\x04\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c/data/plates/2024-03-04_plate_mapping_file13.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00uu\x8c\x10plate13_r128_75k\x94}\x94(\x8c\x05group\x94\x8c\x05pilot\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x03\x04\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c.data/plates/2024-03-04_plate_mapping_file9.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00uu\x8c\x07plate14\x94}\x94(\x8c\x05group\x94\x8c\x0eplatesToRepeat\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x03\x13\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c/data/plates/2024-03-19_plate_mapping_file14.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rbarcode_wells\x94]\x94(]\x94(\x8c\x10AAGTATTGCTACACAT\x94\x8c\x02H3\x94e]\x94(\x8c\x10CCTATAAGGCCTTACG\x94\x8c\x02H3\x94ees\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00uu\x8c\x07plate15\x94}\x94(\x8c\x05group\x94\x8c\x11PennVaccineCohort\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x03\x1c\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c/data/plates/2024-03-28_plate_mapping_file15.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00uu\x8c\x07plate16\x94}\x94(\x8c\x05group\x94\x8c\x11PennVaccineCohort\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x04\t\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c/data/plates/2024-04-09_plate_mapping_file16.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00uu\x8c\x07plate17\x94}\x94(\x8c\x05group\x94\x8c\x03SCH\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x04\x0b\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c/data/plates/2024-04-11_plate_mapping_file17.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\x05wells\x94]\x94\x8c\x02A9\x94as\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00uu\x8c\x07plate18\x94}\x94(\x8c\x05group\x94\x8c\x03SCH\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x04\x0b\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c/data/plates/2024-04-11_plate_mapping_file18.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00uu\x8c\x0cplate19_100k\x94}\x94(\x8c\x05group\x94\x8c\x05pilot\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x04\x10\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c/data/plates/2024-04-23_plate_mapping_file21.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00uu\x8c\x0cplate20_150k\x94}\x94(\x8c\x05group\x94\x8c\x05pilot\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x04\x10\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c/data/plates/2024-04-23_plate_mapping_file22.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00uu\x8c\x0cplate21_200k\x94}\x94(\x8c\x05group\x94\x8c\x05pilot\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x04\x10\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c/data/plates/2024-04-23_plate_mapping_file23.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00uu\x8c\x07plate22\x94}\x94(\x8c\x05group\x94\x8c\x11PennVaccineCohort\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x04\x12\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c/data/plates/2024-04-18_plate_mapping_file19.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00u\x8c\x1eillumina_barcode_parser_params\x94}\x94(\x8c\tupstream2\x94\x8c\x06TGACGC\x94\x8c\x12upstream2_mismatch\x94K\x01uu\x8c\x07plate23\x94}\x94(\x8c\x05group\x94\x8c\x11PennVaccineCohort\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x04\x12\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c/data/plates/2024-04-18_plate_mapping_file20.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00u\x8c\x1eillumina_barcode_parser_params\x94}\x94(\x8c\tupstream2\x94\x8c\x06CAGTTG\x94\x8c\x12upstream2_mismatch\x94K\x01uu\x8c\x07plate24\x94}\x94(\x8c\x05group\x94\x8c\x11PennVaccineCohort\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x05\x02\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c/data/plates/2024-05-02_plate_mapping_file24.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00uu\x8c\x07plate25\x94}\x94(\x8c\x05group\x94\x8c\x03SCH\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x05\x14\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c/data/plates/2024-05-20_plate_mapping_file27.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00u\x8c\x1eillumina_barcode_parser_params\x94}\x94(\x8c\tupstream2\x94\x8c\x06TGACGC\x94\x8c\x12upstream2_mismatch\x94K\x01uu\x8c\x07plate26\x94}\x94(\x8c\x05group\x94\x8c\x03SCH\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x05\x14\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c/data/plates/2024-05-20_plate_mapping_file28.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00u\x8c\x1eillumina_barcode_parser_params\x94}\x94(\x8c\tupstream2\x94\x8c\x06CAGTTG\x94\x8c\x12upstream2_mismatch\x94K\x01uu\x8c\x07plate27\x94}\x94(\x8c\x05group\x94\x8c\x11PennVaccineCohort\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x05\x11\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c/data/plates/2024-05-17_plate_mapping_file26.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?PbM\xd2\xf1\xa9\xfch\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00u\x8c\x1eillumina_barcode_parser_params\x94}\x94(\x8c\tupstream2\x94\x8c\x06GCTACA\x94\x8c\x12upstream2_mismatch\x94K\x01uu\x8c\x07plate28\x94}\x94(\x8c\x05group\x94\x8c\x11PennVaccineCohort\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x05\x11\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c/data/plates/2024-05-17_plate_mapping_file25.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?PbM\xd2\xf1\xa9\xfch\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00u\x8c\x1eillumina_barcode_parser_params\x94}\x94(\x8c\tupstream2\x94\x8c\x06ATCGAT\x94\x8c\x12upstream2_mismatch\x94K\x01uu\x8c\x07plate29\x94}\x94(\x8c\x05group\x94\x8c\x11PennVaccineCohort\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x05\x1c\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c/data/plates/2024-05-28_plate_mapping_file30.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00u\x8c\x1eillumina_barcode_parser_params\x94}\x94(\x8c\tupstream2\x94\x8c\x06TGACGC\x94\x8c\x12upstream2_mismatch\x94K\x01uu\x8c\x07plate30\x94}\x94(\x8c\x05group\x94\x8c\x11PennVaccineCohort\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x05\x1c\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c/data/plates/2024-05-28_plate_mapping_file29.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00u\x8c\x1eillumina_barcode_parser_params\x94}\x94(\x8c\tupstream2\x94\x8c\x06CAGTTG\x94\x8c\x12upstream2_mismatch\x94K\x01uu\x8c\x07plate31\x94}\x94(\x8c\x05group\x94\x8c\x03SCH\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x05\x1e\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c/data/plates/2024-05-30_plate_mapping_file31.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00u\x8c\x1eillumina_barcode_parser_params\x94}\x94(\x8c\tupstream2\x94\x8c\x06GCTACA\x94\x8c\x12upstream2_mismatch\x94K\x01uu\x8c\x07plate32\x94}\x94(\x8c\x05group\x94\x8c\x03SCH\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x05\x1e\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c/data/plates/2024-05-30_plate_mapping_file32.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\x08barcodes\x94]\x94\x8c\x10CCAATCCCAGCCTTTA\x94as\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00u\x8c\x1eillumina_barcode_parser_params\x94}\x94(\x8c\tupstream2\x94\x8c\x06ATCGAT\x94\x8c\x12upstream2_mismatch\x94K\x01uu\x8c\x07plate33\x94}\x94(\x8c\x05group\x94\x8c\x11PennVaccineCohort\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x06\x04\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c/data/plates/2024-06-04_plate_mapping_file34.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00u\x8c\x1eillumina_barcode_parser_params\x94}\x94(\x8c\tupstream2\x94\x8c\x06TGACGC\x94\x8c\x12upstream2_mismatch\x94K\x01uu\x8c\x07plate34\x94}\x94(\x8c\x05group\x94\x8c\x11PennVaccineCohort\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x06\x04\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c/data/plates/2024-06-04_plate_mapping_file33.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00u\x8c\x1eillumina_barcode_parser_params\x94}\x94(\x8c\tupstream2\x94\x8c\x06CAGTTG\x94\x8c\x12upstream2_mismatch\x94K\x01uu\x8c\x07plate35\x94}\x94(\x8c\x05group\x94\x8c\x11PennVaccineCohort\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x06\x04\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c/data/plates/2024-07-02_plate_mapping_file36.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00u\x8c\x1eillumina_barcode_parser_params\x94}\x94(\x8c\tupstream2\x94\x8c\x06ATCGAT\x94\x8c\x12upstream2_mismatch\x94K\x01uu\x8c\x07plate36\x94}\x94(\x8c\x05group\x94\x8c\x03SCH\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x05\x1e\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c/data/plates/2024-07-02_plate_mapping_file35.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00u\x8c\x1eillumina_barcode_parser_params\x94}\x94(\x8c\tupstream2\x94\x8c\x06TGACGC\x94\x8c\x12upstream2_mismatch\x94K\x01uu\x8c\x07plate37\x94}\x94(\x8c\x05group\x94\x8c\x11MA22VaccineCohort\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\t\x18\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c/data/plates/2024-09-24_plate_mapping_file37.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00u\x8c\x1eillumina_barcode_parser_params\x94}\x94(\x8c\tupstream2\x94\x8c\x06ATCGAT\x94\x8c\x12upstream2_mismatch\x94K\x01uu\x8c\x07plate38\x94}\x94(\x8c\x05group\x94\x8c\nPooledSera\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\t\x18\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c/data/plates/2024-09-24_plate_mapping_file38.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00u\x8c\x1eillumina_barcode_parser_params\x94}\x94(\x8c\tupstream2\x94\x8c\x06ATCGAT\x94\x8c\x12upstream2_mismatch\x94K\x01uu\x8c\x07plate39\x94}\x94(\x8c\x05group\x94\x8c\nPooledSera\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\t\x19\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c/data/plates/2024-09-25_plate_mapping_file40.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00u\x8c\x1eillumina_barcode_parser_params\x94}\x94(\x8c\tupstream2\x94\x8c\x06GCTACA\x94\x8c\x12upstream2_mismatch\x94K\x01uu\x8c\x07plate40\x94}\x94(\x8c\x05group\x94\x8c\x11MA22VaccineCohort\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\t\x19\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c/data/plates/2024-09-25_plate_mapping_file41.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\x05wells\x94]\x94(\x8c\x03C10\x94\x8c\x03C11\x94es\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00u\x8c\x1eillumina_barcode_parser_params\x94}\x94(\x8c\tupstream2\x94\x8c\x06GCTACA\x94\x8c\x12upstream2_mismatch\x94K\x01uu\x8c\x07plate41\x94}\x94(\x8c\x05group\x94\x8c\x11MA22VaccineCohort\x94\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\t\x19\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c/data/plates/2024-09-25_plate_mapping_file39.csv\x94\x8c\x0cmanual_drops\x94}\x94\x8c\rqc_thresholds\x94}\x94(h\xc6M\xf4\x01h\xc7G?tz\xe1G\xae\x14{h\xc8}\x94(h\xcaG?\x1a6\xe2\xeb\x1cC-h\xcbK\x04h\xccK\x02uh\xcd}\x94(h\xcfG?tz\xe1G\xae\x14{h\xd0K\x04h\xd1K\x02uh\xd2M\xe8\x03h\xd3Kdh\xd4K\x03h\xd5K\x06u\x8c\x0fcurvefit_params\x94}\x94(h\xd8K\x01h\xd9j\xca\x01\x00\x00h\xdbK\x00h\xdcj\xcb\x01\x00\x00u\x8c\x0bcurvefit_qc\x94}\x94(h\xe0G\x00\x00\x00\x00\x00\x00\x00\x00h\xe1}\x94(h\xe3G?\xe0\x00\x00\x00\x00\x00\x00h\xe4G?\xc3333333uh\xe5j\xcf\x01\x00\x00h\xe7j\xd0\x01\x00\x00u\x8c\x1eillumina_barcode_parser_params\x94}\x94(\x8c\tupstream2\x94\x8c\x06ATCGAT\x94\x8c\x12upstream2_mismatch\x94K\x01uuu\x8c\x14miscellaneous_plates\x94}\x94(\x8c\x13240111_initial_H3N2\x94}\x94(\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x01\x0b\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c=data/miscellaneous_plates/H3N2library_initialPool_samples.csv\x94u\x8c\x12240124_repool_H3N2\x94}\x94(\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x01\x18\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8cGdata/miscellaneous_plates/2024-01-22_H3N2_sampleData_rePool_MOItest.csv\x94u\x8c\x12240207_repool_H3N2\x94}\x94(\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x02\x07\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8cGdata/miscellaneous_plates/2024-02-07_H3N2_sampleData_rePool_MOItest.csv\x94u\x8c\x12240207_repool_H1N1\x94}\x94(\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x02\x07\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH1N1_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8cGdata/miscellaneous_plates/2024-02-07_H1N1_sampleData_rePool_MOItest.csv\x94u\x8c\x1f240328_repool_H3N2_variableCell\x94}\x94(\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\x03\x1c\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8cBdata/miscellaneous_plates/2024-03-28_H3N2_MOItest_variableCell.csv\x94u\x8c\x1c240924_repool_H3N2_balancing\x94}\x94(\x8c\x04date\x94j\xe4\x01\x00\x00C\x04\x07\xe8\t\x18\x94\x85\x94R\x94\x8c\rviral_library\x94\x8c\x0cH3N2_library\x94\x8c\x11neut_standard_set\x94\x8c\x08loes2023\x94\x8c\x0bsamples_csv\x94\x8c>data/miscellaneous_plates/2024-09-24_repool_H3N2_balancing.csv\x94\x8c\x1eillumina_barcode_parser_params\x94}\x94(\x8c\tupstream2\x94\x8c\x06GCTACA\x94\x8c\x12upstream2_mismatch\x94K\x01uuuu\x8c\x04rule\x94\x8c\rprocess_plate\x94\x8c\x0fbench_iteration\x94N\x8c\tscriptdir\x94\x8ck/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/seqneut-pipeline/notebooks\x94ub.'); from snakemake.logging import logger; logger.printshellcmds = False; import os; os.chdir(r'/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024');
######## snakemake preamble end #########
Process plate counts to get fraction infectivities and fit curves¶
This notebook is designed to be run using snakemake, and analyzes a plate of sequencing-based neutralization assays.
The plots generated by this notebook are interactive, so you can mouseover points for details, use the mouse-scroll to zoom and pan, and use interactive dropdowns at the bottom of the plots.
Setup¶
Import Python modules:
import pickle
import sys
import altair as alt
import matplotlib.pyplot as plt
import neutcurve
import numpy
import pandas as pd
import ruamel.yaml as yaml
_ = alt.data_transformers.disable_max_rows()
Get the variables passed by snakemake:
count_csvs = snakemake.input.count_csvs
fate_csvs = snakemake.input.fate_csvs
viral_library_csv = snakemake.input.viral_library_csv
neut_standard_set_csv = snakemake.input.neut_standard_set_csv
qc_drops_yaml = snakemake.output.qc_drops
frac_infectivity_csv = snakemake.output.frac_infectivity_csv
fits_csv = snakemake.output.fits_csv
fits_pickle = snakemake.output.fits_pickle
samples = snakemake.params.samples
plate = snakemake.wildcards.plate
plate_params = snakemake.params.plate_params
# get thresholds turning lists into tuples as needed
manual_drops = {
filter_type: [tuple(w) if isinstance(w, list) else w for w in filter_drops]
for (filter_type, filter_drops) in plate_params["manual_drops"].items()
}
group = plate_params["group"]
qc_thresholds = plate_params["qc_thresholds"]
curvefit_params = plate_params["curvefit_params"]
curvefit_qc = plate_params["curvefit_qc"]
curvefit_qc["barcode_serum_replicates_ignore_curvefit_qc"] = [
tuple(w) for w in curvefit_qc["barcode_serum_replicates_ignore_curvefit_qc"]
]
print(f"Processing {plate=}")
samples_df = pd.DataFrame(plate_params["samples"])
print(f"\nPlate has {len(samples)} samples (wells)")
assert all(
(len(samples_df) == samples_df[c].nunique())
for c in ["well", "sample", "sample_noplate"]
)
assert len(samples_df) == len(
samples_df.groupby(["serum_replicate", "dilution_factor"])
)
assert len(samples) == len(count_csvs) == len(fate_csvs) == len(samples_df)
for d, key, title in [
(manual_drops, "manual_drops", "Data manually specified to drop:"),
(qc_thresholds, "qc_thresholds", "QC thresholds applied to data:"),
(curvefit_params, "curvefit_params", "Curve-fitting parameters:"),
(curvefit_qc, "curvefit_qc", "Curve-fitting QC:"),
]:
print(f"\n{title}")
yaml.YAML(typ="rt").dump({key: d}, stream=sys.stdout)
Processing plate='plate2'
Plate has 28 samples (wells)
Data manually specified to drop:
manual_drops: {}
QC thresholds applied to data:
qc_thresholds:
avg_barcode_counts_per_well: 500
min_neut_standard_frac_per_well: 0.005
no_serum_per_viral_barcode_filters:
min_frac: 0.0001
max_fold_change: 4
max_wells: 2
per_neut_standard_barcode_filters:
min_frac: 0.005
max_fold_change: 4
max_wells: 2
min_neut_standard_count_per_well: 1000
min_no_serum_count_per_viral_barcode_well: 100
max_frac_infectivity_per_viral_barcode_well: 3
min_dilutions_per_barcode_serum_replicate: 6
Curve-fitting parameters: curvefit_params: frac_infectivity_ceiling: 1 fixtop: - 0.6 - 1 fixbottom: 0 fixslope: - 0.8 - 10
Curve-fitting QC:
curvefit_qc:
max_frac_infectivity_at_least: 0.0
goodness_of_fit:
min_R2: 0.5
max_RMSD: 0.15
serum_replicates_ignore_curvefit_qc: []
barcode_serum_replicates_ignore_curvefit_qc: []
Set up dictionary to keep track of wells, barcodes, well-barcodes, and serum-replicates that are dropped:
qc_drops = {
"wells": {},
"barcodes": {},
"barcode_wells": {},
"barcode_serum_replicates": {},
"serum_replicates": {},
}
assert set(manual_drops).issubset(
qc_drops
), f"{manual_drops.keys()=}, {qc_drops.keys()}"
Statistics on barcode-parsing for each sample¶
Make interactive chart of the "fates" of the sequencing reads parsed for each sample on the plate.
If most sequencing reads are not "valid barcodes", this could potentially indicate some problem in the sequencing or barcode set you are parsing.
Potential fates are:
- valid barcode: barcode that matches a known virus or neutralization standard, we hope most reads are this.
- invalid barcode: a barcode with proper flanking sequences, but does not match a known virus or neutralization standard. If you have a lot of reads of this type, it is probably a good idea to look at the invalid barcode CSVs (in the
./results/barcode_invalid/subdirectory created by the pipeline) to see what these invalid barcodes are. - unparseable barcode: could not parse a barcode from this read as there was not a sequence of the correct length with the appropriate flanking sequence.
- invalid outer flank: if using an outer upstream or downstream region (
upstream2ordownstream2for the illuminabarcodeparser), reads that are otherwise valid except for this outer flank. Typically you would be usingupstream2if you have a plate index embedded in your primer, and reads with this classification correspond to a different index than the one for this plate. - low quality barcode: low-quality or
Nnucleotides in barcode, could indicate problem with sequencing. - failed chastity filter: reads that failed the Illumina chastity filter, if these are reported in the FASTQ (they may not be).
Also, if the number of reads per sample is very uneven, that could indicate that you did not do a good job of balancing the different samples in the Illumina sequencing.
fates = (
pd.concat([pd.read_csv(f).assign(sample=s) for f, s in zip(fate_csvs, samples)])
.merge(samples_df, validate="many_to_one", on="sample")
.assign(
fate_counts=lambda x: x.groupby("fate")["count"].transform("sum"),
sample_well=lambda x: x["sample_noplate"] + " (" + x["well"] + ")",
)
.query("fate_counts > 0")[ # only keep fates with at least one count
["fate", "count", "well", "serum_replicate", "sample_well", "dilution_factor"]
]
)
assert len(fates) == len(fates.drop_duplicates())
serum_replicates = sorted(fates["serum_replicate"].unique())
sample_wells = list(
fates.sort_values(["serum_replicate", "dilution_factor"])["sample_well"]
)
serum_selection = alt.selection_point(
fields=["serum_replicate"],
bind=alt.binding_select(
options=[None] + serum_replicates,
labels=["all"] + serum_replicates,
name="serum",
),
)
fates_chart = (
alt.Chart(fates)
.add_params(serum_selection)
.transform_filter(serum_selection)
.encode(
alt.X("count", scale=alt.Scale(nice=False, padding=3)),
alt.Y(
"sample_well",
title=None,
sort=sample_wells,
),
alt.Color("fate", sort=sorted(fates["fate"].unique(), reverse=True)),
alt.Order("fate", sort="descending"),
tooltip=fates.columns.tolist(),
)
.mark_bar(height={"band": 0.85})
.properties(
height=alt.Step(10),
width=200,
title=f"Barcode parsing for {plate}",
)
.configure_axis(grid=False)
)
fates_chart
Read barcode counts and apply manually specified drops¶
Read the counts per barcode:
# get barcode counts
counts = (
pd.concat([pd.read_csv(c).assign(sample=s) for c, s in zip(count_csvs, samples)])
.merge(samples_df, validate="many_to_one", on="sample")
.drop(columns=["replicate", "plate", "fastq"])
.assign(sample_well=lambda x: x["sample_noplate"] + " (" + x["well"] + ")")
)
# classify barcodes as viral or neut standard
barcode_class = pd.concat(
[
pd.read_csv(viral_library_csv)[["barcode", "strain"]].assign(
neut_standard=False,
),
pd.read_csv(neut_standard_set_csv)[["barcode"]].assign(
neut_standard=True,
strain=pd.NA,
),
],
ignore_index=True,
)
# merge counts and classification of barcodes
assert set(counts["barcode"]) == set(barcode_class["barcode"])
counts = counts.merge(barcode_class, on="barcode", validate="many_to_one")
assert set(sample_wells) == set(counts["sample_well"])
assert set(serum_replicates) == set(counts["serum_replicate"])
Apply any manually specified data drops:
for filter_type, filter_drops in manual_drops.items():
print(f"\nDropping {len(filter_drops)} {filter_type} specified in manual_drops")
assert filter_type in qc_drops
qc_drops[filter_type].update(
{w: "manual_drop" for w in filter_drops if not isinstance(w, list)}
)
if filter_type == "barcode_wells":
counts = counts[
~counts.assign(
barcode_well=lambda x: x.apply(
lambda r: (r["barcode"], r["well"]), axis=1
)
)["barcode_well"].isin(qc_drops[filter_type])
]
elif filter_type == "barcode_serum_replicates":
counts = counts[
~counts.assign(
barcode_serum_replicate=lambda x: x.apply(
lambda r: (r["barcode"], r["serum_replicate"]), axis=1
)
)["barcode_serum_replicate"].isin(qc_drops[filter_type])
]
elif filter_type == "wells":
counts = counts[~counts["well"].isin(qc_drops[filter_type])]
elif filter_type == "barcodes":
counts = counts[~counts["barcode"].isin(qc_drops[filter_type])]
else:
assert filter_type in set(counts.columns)
counts = counts[~counts[filter_type].isin(qc_drops[filter_type])]
Average counts per barcode in each well¶
Plot average counts per barcode. If a sample has inadequate barcode counts, it may not have good enough statistics for accurate analysis, and a QC-threshold is applied:
avg_barcode_counts = (
counts.groupby(
["well", "serum_replicate", "sample_well"],
dropna=False,
as_index=False,
)
.aggregate(avg_count=pd.NamedAgg("count", "mean"))
.assign(
fails_qc=lambda x: (
x["avg_count"] < qc_thresholds["avg_barcode_counts_per_well"]
),
)
)
avg_barcode_counts_chart = (
alt.Chart(avg_barcode_counts)
.add_params(serum_selection)
.transform_filter(serum_selection)
.encode(
alt.X(
"avg_count",
title="average barcode counts per well",
scale=alt.Scale(nice=False, padding=3),
),
alt.Y("sample_well", sort=sample_wells),
alt.Color(
"fails_qc",
title=f"fails {qc_thresholds['avg_barcode_counts_per_well']=}",
legend=alt.Legend(titleLimit=500),
),
tooltip=[
alt.Tooltip(c, format=".3g") if avg_barcode_counts[c].dtype == float else c
for c in avg_barcode_counts.columns
],
)
.mark_bar(height={"band": 0.85})
.properties(
height=alt.Step(10),
width=250,
title=f"Average barcode counts per well for {plate}",
)
.configure_axis(grid=False)
)
display(avg_barcode_counts_chart)
# drop wells failing QC
avg_barcode_counts_per_well_drops = list(avg_barcode_counts.query("fails_qc")["well"])
print(
f"\nDropping {len(avg_barcode_counts_per_well_drops)} wells for failing "
f"{qc_thresholds['avg_barcode_counts_per_well']=}: "
+ str(avg_barcode_counts_per_well_drops)
)
qc_drops["wells"].update(
{w: "avg_barcode_counts_per_well" for w in avg_barcode_counts_per_well_drops}
)
counts = counts[~counts["well"].isin(qc_drops["wells"])]
Dropping 0 wells for failing qc_thresholds['avg_barcode_counts_per_well']=500: []
Fraction of counts from neutralization standard¶
Determine the fraction of counts from the neutralization standard in each sample, and make sure this fraction passess the QC threshold.
neut_standard_fracs = (
counts.assign(
neut_standard_count=lambda x: x["count"] * x["neut_standard"].astype(int)
)
.groupby(
["well", "serum_replicate", "sample_well"],
dropna=False,
as_index=False,
)
.aggregate(
total_count=pd.NamedAgg("count", "sum"),
neut_standard_count=pd.NamedAgg("neut_standard_count", "sum"),
)
.assign(
neut_standard_frac=lambda x: x["neut_standard_count"] / x["total_count"],
fails_qc=lambda x: (
x["neut_standard_frac"] < qc_thresholds["min_neut_standard_frac_per_well"]
),
)
)
neut_standard_fracs_chart = (
alt.Chart(neut_standard_fracs)
.add_params(serum_selection)
.transform_filter(serum_selection)
.encode(
alt.X(
"neut_standard_frac",
title="frac counts from neutralization standard per well",
scale=alt.Scale(nice=False, padding=3),
),
alt.Y("sample_well", sort=sample_wells),
alt.Color(
"fails_qc",
title=f"fails {qc_thresholds['min_neut_standard_frac_per_well']=}",
legend=alt.Legend(titleLimit=500),
),
tooltip=[
alt.Tooltip(c, format=".3g") if neut_standard_fracs[c].dtype == float else c
for c in neut_standard_fracs.columns
],
)
.mark_bar(height={"band": 0.85})
.properties(
height=alt.Step(10),
width=250,
title=f"Neutralization-standard fracs per well for {plate}",
)
.configure_axis(grid=False)
.configure_legend(titleLimit=1000)
)
display(neut_standard_fracs_chart)
# drop wells failing QC
min_neut_standard_frac_per_well_drops = list(
neut_standard_fracs.query("fails_qc")["well"]
)
print(
f"\nDropping {len(min_neut_standard_frac_per_well_drops)} wells for failing "
f"{qc_thresholds['min_neut_standard_frac_per_well']=}: "
+ str(min_neut_standard_frac_per_well_drops)
)
qc_drops["wells"].update(
{
w: "min_neut_standard_frac_per_well"
for w in min_neut_standard_frac_per_well_drops
}
)
counts = counts[~counts["well"].isin(qc_drops["wells"])]
Dropping 0 wells for failing qc_thresholds['min_neut_standard_frac_per_well']=0.005: []
Consistency and minimum fractions for barcodes¶
We examine the fraction of counts attributable to each barcode. We do this splitting the data two ways:
Looking at all viral (but not neut-standard) barcodes only for the no-serum samples (wells).
Looking at just the neut-standard barcodes for all samples (wells).
The reasons is that if the experiment is set up perfectly, these fractions should be the same across all samples for each barcode. (We do not expect viral barcodes to have consistent fractions across no-serum samples as they will be neutralized differently depending on strain).
We plot these fractions in interactive plots (you can mouseover points and zoom) so you can identify barcodes that fail the expected consistency QC thresholds.
We also make sure the barcodes meet specified QC minimum thresholds for all samples, and flag any that do not.
barcode_selection = alt.selection_point(fields=["barcode"], on="mouseover", empty=False)
# look at all samples for neut standard barcodes, or no-serum samples for all barcodes
for is_neut_standard, df in counts.groupby("neut_standard"):
if is_neut_standard:
print(
f"\n\n{'=' * 89}\nAnalyzing neut-standard barcodes from all samples (wells)"
)
qc_name = "per_neut_standard_barcode_filters"
else:
print(f"\n\n{'=' * 89}\nAnalyzing all barcodes from no-serum samples (wells)")
qc_name = "no_serum_per_viral_barcode_filters"
df = df.query("serum == 'none'")
df = df.assign(
sample_counts=lambda x: x.groupby("sample")["count"].transform("sum"),
count_frac=lambda x: x["count"] / x["sample_counts"],
median_count_frac=lambda x: x.groupby("barcode")["count_frac"].transform(
"median"
),
fold_change_from_median=lambda x: numpy.where(
x["count_frac"] > x["median_count_frac"],
x["count_frac"] / x["median_count_frac"],
x["median_count_frac"] / x["count_frac"],
),
)[
[
"barcode",
"count",
"well",
"sample_well",
"count_frac",
"median_count_frac",
"fold_change_from_median",
]
+ ([] if is_neut_standard else ["strain"])
]
# barcode fails QC if fails in sufficient wells
qc = qc_thresholds[qc_name]
print(f"Apply QC {qc_name}: {qc}\n")
fails_qc = (
df.assign(
fails_qc=lambda x: ~(
(x["count_frac"] >= qc["min_frac"])
& (x["fold_change_from_median"] <= qc["max_fold_change"])
),
)
.groupby("barcode", as_index=False)
.aggregate(n_wells_fail_qc=pd.NamedAgg("fails_qc", "sum"))
.assign(fails_qc=lambda x: x["n_wells_fail_qc"] >= qc["max_wells"])[
["barcode", "fails_qc"]
]
)
df = df.merge(fails_qc, on="barcode", validate="many_to_one")
# make chart
evenness_chart = (
alt.Chart(df)
.add_params(barcode_selection)
.encode(
alt.X(
"count_frac",
title=(
"barcode's fraction of neut standard counts"
if is_neut_standard
else "barcode's fraction of non-neut standard counts"
),
scale=alt.Scale(nice=False, padding=5),
),
alt.Y("sample_well", sort=sample_wells),
alt.Fill(
"fails_qc",
title=f"fails {qc_name}",
legend=alt.Legend(titleLimit=500),
),
strokeWidth=alt.condition(barcode_selection, alt.value(2), alt.value(0)),
size=alt.condition(barcode_selection, alt.value(60), alt.value(35)),
tooltip=[
alt.Tooltip(c, format=".2g") if df[c].dtype == float else c
for c in df.columns
],
)
.mark_circle(fillOpacity=0.45, stroke="black", strokeOpacity=1)
.properties(
height=alt.Step(10),
width=300,
title=alt.TitleParams(
(
f"{plate} all samples, neut-standard barcodes"
if is_neut_standard
else f"{plate} no-serum samples, all barcodes"
),
subtitle="x-axis is zoomable (use mouse scroll/pan)",
),
)
.configure_axis(grid=False)
.configure_legend(titleLimit=1000)
.interactive()
)
display(evenness_chart)
# drop barcodes failing QC
barcode_drops = list(fails_qc.query("fails_qc")["barcode"])
print(
f"\nDropping {len(barcode_drops)} barcodes for failing {qc=}: {barcode_drops}"
)
qc_drops["barcodes"].update(
{bc: "min_neut_standard_frac_per_well" for bc in barcode_drops}
)
counts = counts[~counts["barcode"].isin(qc_drops["barcodes"])]
=========================================================================================
Analyzing all barcodes from no-serum samples (wells)
Apply QC no_serum_per_viral_barcode_filters: {'min_frac': 0.0001, 'max_fold_change': 4, 'max_wells': 2}
Dropping 12 barcodes for failing qc={'min_frac': 0.0001, 'max_fold_change': 4, 'max_wells': 2}: ['AAAGTAGCAGAGGATT', 'AAATTCACAATATCCA', 'AGACCATCGCACCCAA', 'ATAACGTTTGTGCAAA', 'CAAAAGCAGCACGATA', 'CATAAAAGACTGTATA', 'CGTACGTATGTCCCAG', 'CGTCCCTGGCGTGTCG', 'CGTTAACGGCCTATCC', 'TATATGGAATACTAAA', 'TCTCCGATAGCCCTAC', 'TGTTGTAATCTGAATA']
=========================================================================================
Analyzing neut-standard barcodes from all samples (wells)
Apply QC per_neut_standard_barcode_filters: {'min_frac': 0.005, 'max_fold_change': 4, 'max_wells': 2}
Dropping 0 barcodes for failing qc={'min_frac': 0.005, 'max_fold_change': 4, 'max_wells': 2}: []
Compute fraction infectivity¶
The fraction infectivity for viral barcode $v_b$ in sample $s$ is computed as: $$ F_{v_b,s} = \frac{c_{v_b,s} / \left(\sum_{n_b} c_{n_b,s}\right)}{{\rm median}_{s_0}\left[ c_{v_b,s_0} / \left(\sum_{n_b} c_{n_b,s_0}\right)\right]} $$ where
- $c_{v_b,s}$ is the counts of viral barcode $v_b$ in sample $s$.
- $\sum_{n_b} c_{n_b,s}$ is the sum of the counts for all neutralization standard barcodes $n_b$ for sample $s$.
- $c_{v_b,s_0}$ is the counts of viral barcode $v_b$ in no-serum sample $s_0$.
- $\sum_{n_b} c_{n_b,s_0}$ is the sum of the counts for all neutralization standard barcodes $n_b$ for no-serum sample $s_0$.
- ${\rm median}_{s_0}\left[ c_{v_b,s_0} / \left(\sum_{n_b} c_{n_b,s_0}\right)\right]$ is the median taken across all no-serum samples of the counts of viral barcode $v_b$ versus the total counts for all neutralization standard barcodes.
First, compute the total neutralization-standard counts for each sample (well). Plot these, and drop any wells that do not meet the QC threshold.
neut_standard_counts = (
counts.query("neut_standard")
.groupby(
["well", "serum_replicate", "sample_well", "dilution_factor"],
dropna=False,
as_index=False,
)
.aggregate(neut_standard_count=pd.NamedAgg("count", "sum"))
.assign(
fails_qc=lambda x: (
x["neut_standard_count"] < qc_thresholds["min_neut_standard_count_per_well"]
),
)
)
neut_standard_counts_chart = (
alt.Chart(neut_standard_counts)
.add_params(serum_selection)
.transform_filter(serum_selection)
.encode(
alt.X(
"neut_standard_count",
title="counts from neutralization standard",
scale=alt.Scale(nice=False, padding=3),
),
alt.Y("sample_well", sort=sample_wells),
alt.Color(
"fails_qc",
title=f"fails {qc_thresholds['min_neut_standard_count_per_well']=}",
legend=alt.Legend(titleLimit=500),
),
tooltip=[
(
alt.Tooltip(c, format=".3g")
if neut_standard_counts[c].dtype == float
else c
)
for c in neut_standard_counts.columns
],
)
.mark_bar(height={"band": 0.85})
.properties(
height=alt.Step(10),
width=250,
title=f"Neutralization-standard counts for {plate}",
)
.configure_axis(grid=False)
.configure_legend(titleLimit=1000)
)
display(neut_standard_counts_chart)
# drop wells failing QC
min_neut_standard_count_per_well_drops = list(
neut_standard_counts.query("fails_qc")["well"]
)
print(
f"\nDropping {len(min_neut_standard_count_per_well_drops)} wells for failing "
f"{qc_thresholds['min_neut_standard_count_per_well']=}: "
+ str(min_neut_standard_count_per_well_drops)
)
qc_drops["wells"].update(
{
w: "min_neut_standard_count_per_well"
for w in min_neut_standard_count_per_well_drops
}
)
neut_standard_counts = neut_standard_counts[
~neut_standard_counts["well"].isin(qc_drops["wells"])
]
counts = counts[~counts["well"].isin(qc_drops["wells"])]
Dropping 0 wells for failing qc_thresholds['min_neut_standard_count_per_well']=1000: []
Compute and plot the no-serum sample viral barcode counts and check if they pass the QC filters.
no_serum_counts = (
counts.query("serum == 'none'")
.query("not neut_standard")
.merge(neut_standard_counts, validate="many_to_one")[
["barcode", "strain", "well", "sample_well", "count", "neut_standard_count"]
]
.assign(
fails_qc=lambda x: (
x["count"] <= qc_thresholds["min_no_serum_count_per_viral_barcode_well"]
),
)
)
strains = sorted(no_serum_counts["strain"].unique())
strain_selection_dropdown = alt.selection_point(
fields=["strain"],
bind=alt.binding_select(
options=[None] + strains,
labels=["all"] + strains,
name="virus strain",
),
)
# make chart
no_serum_counts_chart = (
alt.Chart(no_serum_counts)
.add_params(barcode_selection, strain_selection_dropdown)
.transform_filter(strain_selection_dropdown)
.encode(
alt.X(
"count", title="viral barcode count", scale=alt.Scale(nice=False, padding=5)
),
alt.Y("sample_well", sort=sample_wells),
alt.Fill(
"fails_qc",
title=f"fails {qc_thresholds['min_no_serum_count_per_viral_barcode_well']=}",
legend=alt.Legend(titleLimit=500),
),
strokeWidth=alt.condition(barcode_selection, alt.value(2), alt.value(0)),
size=alt.condition(barcode_selection, alt.value(60), alt.value(35)),
tooltip=no_serum_counts.columns.tolist(),
)
.mark_circle(fillOpacity=0.6, stroke="black", strokeOpacity=1)
.properties(
height=alt.Step(10),
width=400,
title=f"{plate} viral barcode counts in no-serum samples",
)
.configure_axis(grid=False)
.configure_legend(titleLimit=1000)
.interactive()
)
display(no_serum_counts_chart)
# drop barcode / wells failing QC
min_no_serum_count_per_viral_barcode_well_drops = list(
no_serum_counts.query("fails_qc")[["barcode", "well"]].itertuples(
index=False, name=None
)
)
print(
f"\nDropping {len(min_no_serum_count_per_viral_barcode_well_drops)} barcode-wells for failing "
f"{qc_thresholds['min_no_serum_count_per_viral_barcode_well']=}: "
+ str(min_no_serum_count_per_viral_barcode_well_drops)
)
qc_drops["barcode_wells"].update(
{
w: "min_no_serum_count_per_viral_barcode_well"
for w in min_no_serum_count_per_viral_barcode_well_drops
}
)
no_serum_counts = no_serum_counts[
~no_serum_counts.assign(
barcode_well=lambda x: x.apply(lambda r: (r["barcode"], r["well"]), axis=1)
)["barcode_well"].isin(qc_drops["barcode_wells"])
]
counts = counts[
~counts.assign(
barcode_well=lambda x: x.apply(lambda r: (r["barcode"], r["well"]), axis=1)
)["barcode_well"].isin(qc_drops["barcode_wells"])
]
Dropping 0 barcode-wells for failing qc_thresholds['min_no_serum_count_per_viral_barcode_well']=100: []
Compute and plot the median ratio of viral barcode count to neut standard counts across no-serum samples. If library composition is equal, all of these values should be similar:
median_no_serum_ratio = (
no_serum_counts.assign(ratio=lambda x: x["count"] / x["neut_standard_count"])
.groupby(["barcode", "strain"], as_index=False)
.aggregate(median_no_serum_ratio=pd.NamedAgg("ratio", "median"))
)
strain_selection = alt.selection_point(fields=["strain"], on="mouseover", empty=False)
median_no_serum_ratio_chart = (
alt.Chart(median_no_serum_ratio)
.add_params(strain_selection)
.encode(
alt.X(
"median_no_serum_ratio",
title="median ratio of counts",
scale=alt.Scale(nice=False, padding=5),
),
alt.Y(
"barcode",
sort=alt.SortField("median_no_serum_ratio", order="descending"),
axis=alt.Axis(labelFontSize=5),
),
color=alt.condition(strain_selection, alt.value("orange"), alt.value("gray")),
tooltip=[
(
alt.Tooltip(c, format=".3g")
if median_no_serum_ratio[c].dtype == float
else c
)
for c in median_no_serum_ratio.columns
],
)
.mark_bar(height={"band": 0.85})
.properties(
height=alt.Step(5),
width=250,
title=f"{plate} no-serum median ratio viral barcode to neut-standard barcode",
)
.configure_axis(grid=False)
.configure_legend(titleLimit=1000)
)
display(median_no_serum_ratio_chart)
Compute the actual fraction infectivities. We compute both the raw fraction infectivities and the ones with the ceiling applied:
frac_infectivity = (
counts.query("not neut_standard")
.query("serum != 'none'")
.merge(median_no_serum_ratio, validate="many_to_one")
.merge(neut_standard_counts, validate="many_to_one")
.assign(
frac_infectivity_raw=lambda x: (
(x["count"] / x["neut_standard_count"]) / x["median_no_serum_ratio"]
),
frac_infectivity_ceiling=lambda x: x["frac_infectivity_raw"].clip(
upper=curvefit_params["frac_infectivity_ceiling"]
),
concentration=lambda x: 1 / x["dilution_factor"],
plate_barcode=lambda x: x["plate_replicate"] + "-" + x["barcode"],
)[
[
"barcode",
"plate_barcode",
"well",
"strain",
"serum",
"serum_replicate",
"dilution_factor",
"concentration",
"frac_infectivity_raw",
"frac_infectivity_ceiling",
]
]
)
assert len(
frac_infectivity.groupby(["serum", "plate_barcode", "dilution_factor"])
) == len(frac_infectivity)
assert frac_infectivity["dilution_factor"].notnull().all()
assert frac_infectivity["frac_infectivity_raw"].notnull().all()
assert frac_infectivity["frac_infectivity_ceiling"].notnull().all()
Plot the fraction infectivities, both the raw values and with the ceiling applied:
frac_infectivity_chart_df = (
frac_infectivity.assign(
fails_qc=lambda x: (
x["frac_infectivity_raw"]
> qc_thresholds["max_frac_infectivity_per_viral_barcode_well"]
),
)
.melt(
id_vars=[
"barcode",
"strain",
"well",
"serum_replicate",
"dilution_factor",
"fails_qc",
],
value_vars=["frac_infectivity_raw", "frac_infectivity_ceiling"],
var_name="ceiling_applied",
value_name="frac_infectivity",
)
.assign(
ceiling_applied=lambda x: x["ceiling_applied"].map(
{
"frac_infectivity_raw": "raw fraction infectivity",
"frac_infectivity_ceiling": f"fraction infectivity with ceiling at {curvefit_params['frac_infectivity_ceiling']}",
}
)
)
)
frac_infectivity_chart = (
alt.Chart(frac_infectivity_chart_df)
.add_params(strain_selection_dropdown, barcode_selection)
.transform_filter(strain_selection_dropdown)
.encode(
alt.X(
"dilution_factor",
title="dilution factor",
scale=alt.Scale(nice=False, padding=5, type="log"),
),
alt.Y(
"frac_infectivity",
title="fraction infectivity",
scale=alt.Scale(nice=False, padding=5),
),
alt.Column(
"ceiling_applied",
sort="descending",
title=None,
header=alt.Header(labelFontSize=13, labelFontStyle="bold", labelPadding=2),
),
alt.Row(
"serum_replicate",
title=None,
spacing=3,
header=alt.Header(labelFontSize=13, labelFontStyle="bold"),
),
alt.Detail("barcode"),
alt.Shape(
"fails_qc",
title=f"fails {qc_thresholds['max_frac_infectivity_per_viral_barcode_well']=}",
legend=alt.Legend(titleLimit=500, orient="bottom"),
),
color=alt.condition(
barcode_selection, alt.value("black"), alt.value("MediumBlue")
),
strokeWidth=alt.condition(barcode_selection, alt.value(3), alt.value(1)),
opacity=alt.condition(barcode_selection, alt.value(1), alt.value(0.25)),
tooltip=[
(
alt.Tooltip(c, format=".3g")
if frac_infectivity_chart_df[c].dtype == float
else c
)
for c in frac_infectivity_chart_df.columns
],
)
.mark_line(point=True)
.properties(
height=150,
width=250,
title=f"Fraction infectivities for {plate}",
)
.interactive(bind_x=False)
.configure_axis(grid=False)
.configure_legend(titleLimit=1000)
.configure_point(size=50)
.resolve_scale(x="independent", y="independent")
)
display(frac_infectivity_chart)
# drop barcode / wells failing QC
max_frac_infectivity_per_viral_barcode_well_drops = list(
frac_infectivity_chart_df.query("fails_qc")[["barcode", "well"]]
.drop_duplicates()
.itertuples(index=False, name=None)
)
print(
f"\nDropping {len(max_frac_infectivity_per_viral_barcode_well_drops)} barcode-wells for failing "
f"{qc_thresholds['max_frac_infectivity_per_viral_barcode_well']=}: "
+ str(max_frac_infectivity_per_viral_barcode_well_drops)
)
qc_drops["barcode_wells"].update(
{
w: "max_frac_infectivity_per_viral_barcode_well"
for w in max_frac_infectivity_per_viral_barcode_well_drops
}
)
frac_infectivity = frac_infectivity[
~frac_infectivity.assign(
barcode_well=lambda x: x.apply(lambda r: (r["barcode"], r["well"]), axis=1)
)["barcode_well"].isin(qc_drops["barcode_wells"])
]
Dropping 180 barcode-wells for failing qc_thresholds['max_frac_infectivity_per_viral_barcode_well']=3: [('GCTAATTCCAAAAGCG', 'D2'), ('TAGTTGCCCCGACCTG', 'D2'), ('TGATCCGCAAGCTTAG', 'D2'), ('GCTAATTCCAAAAGCG', 'D3'), ('CTGAAACCTTGTCCTA', 'D3'), ('CCAATAAAATACGATG', 'D3'), ('TTAGTCATCTGGGTGC', 'D3'), ('CCCCAGGTATAAAATA', 'D3'), ('ACTGAACAGTATAACT', 'D3'), ('TATCATTTCATCTACA', 'D3'), ('GTTATTATGACTTCAT', 'D3'), ('GAAAATCGAGCTTTAA', 'D3'), ('AAACTTCGTGGTATAC', 'D3'), ('AAAGATAAATTCAAAA', 'D3'), ('AACGACACTTACATCC', 'D3'), ('CTATCTTAATCTACAG', 'D3'), ('TCCAAACAGCGTTAAA', 'D3'), ('AATATACCGGCACTAC', 'D3'), ('TCAATGAATGCGGGGT', 'D3'), ('AGATCATAAGCAATAA', 'D3'), ('CCACGTTCATTAGATG', 'D3'), ('TATCGCAATATGATAA', 'D3'), ('CGCAAGGGATACTAAC', 'D3'), ('CGTCAGAAGTTTATAA', 'D3'), ('TCTTAACTACCCGATG', 'D3'), ('TTATGTTTTAATGGTA', 'D3'), ('AACAATTAATTTTTCA', 'D3'), ('TAGTTGCCCCGACCTG', 'D3'), ('GCTGGTGCACAAGATT', 'D3'), ('TGACAAACACCTGAGG', 'D3'), ('CAGCCGCTAAAATGAT', 'D3'), ('CCGCAATGACAATTTG', 'D3'), ('AATCTTTCCAATCTTG', 'D3'), ('CCAGAGACACGCTAGG', 'D3'), ('CCCGCTAACCCTGTCT', 'D3'), ('GCTAATTCCAAAAGCG', 'D4'), ('CCAATAAAATACGATG', 'D4'), ('CCACGTTCATTAGATG', 'D4'), ('AAACTTCGTGGTATAC', 'D4'), ('TTACGTCAATGTTTGA', 'D4'), ('AACAATTAATTTTTCA', 'D4'), ('CAAAAAGCTAATAAGT', 'D4'), ('TCGCGGTAGATTTGCG', 'D4'), ('TCTTAACTACCCGATG', 'D4'), ('CTCCAATAGGAGACGA', 'D4'), ('AATCTTTCCAATCTTG', 'D4'), ('AACCACCCCAGAGATG', 'D10'), ('GCTAATTCCAAAAGCG', 'G2'), ('GCATTATAATCTTGTG', 'G2'), ('TACCATTTTGGTCCGC', 'G2'), ('CCAATAAAATACGATG', 'G2'), ('TCTTAACTACCCGATG', 'G2'), ('AATCTTTCCAATCTTG', 'G2'), ('TAAAAGAATGATGGTC', 'G3'), ('CTGAAACCTTGTCCTA', 'G3'), ('ATCGAAAAAACTGCAA', 'G3'), ('TGTAGTATAAGAATAA', 'G3'), ('GCATTATAATCTTGTG', 'G3'), ('TATCAATTCGGTATTA', 'G3'), ('TACCATTTTGGTCCGC', 'G3'), ('TTGCTAGTCTACCTGA', 'G3'), ('GCTAATTCCAAAAGCG', 'G3'), ('AGTTATGTAAAACGTG', 'G3'), ('TTAGTCATCTGGGTGC', 'G3'), ('CCAATAAAATACGATG', 'G3'), ('GAAAATCGAGCTTTAA', 'G3'), ('AAAGATAAATTCAAAA', 'G3'), ('GTTATTATGACTTCAT', 'G3'), ('TATCATTTCATCTACA', 'G3'), ('ACGTAAATCCCCACAA', 'G3'), ('CCACAAGTTTGAAAAC', 'G3'), ('TAGCTGGGCAAAGGCT', 'G3'), ('TCAAACTATGATATTC', 'G3'), ('CTATCTTAATCTACAG', 'G3'), ('CAAGAAATGTAGTGAA', 'G3'), ('AAACTTCGTGGTATAC', 'G3'), ('TCCAAACAGCGTTAAA', 'G3'), ('GACAGAAACAAAATTA', 'G3'), ('TGCCGATCCAATTGAT', 'G3'), ('AATATACCGGCACTAC', 'G3'), ('TATCGCAATATGATAA', 'G3'), ('CCATCACCTTATACAC', 'G3'), ('CCACGTTCATTAGATG', 'G3'), ('CAAGACAAGCCCTATA', 'G3'), ('CGCAAGGGATACTAAC', 'G3'), ('TCTTAACTACCCGATG', 'G3'), ('TAGATAATAAGATTCA', 'G3'), ('TGGCTAGCGCACACCA', 'G3'), ('AACAATTAATTTTTCA', 'G3'), ('TCTTGAATTTCATGGA', 'G3'), ('CGTCAGAAGTTTATAA', 'G3'), ('ATGGTTATCTTACCTT', 'G3'), ('AATCTTTCCAATCTTG', 'G3'), ('CATAATGCACAAACGC', 'G3'), ('TAGTTGCCCCGACCTG', 'G3'), ('GCTGGTGCACAAGATT', 'G3'), ('CCGCAATGACAATTTG', 'G3'), ('CAATTCGCCGTTCCCC', 'G3'), ('TAAAAGAATGATGGTC', 'G4'), ('ATCGAAAAAACTGCAA', 'G4'), ('GCATTATAATCTTGTG', 'G4'), ('AGTTATGTAAAACGTG', 'G4'), ('TACCATTTTGGTCCGC', 'G4'), ('TTAGTCATCTGGGTGC', 'G4'), ('TATTATCTAAACGGCG', 'G4'), ('GCTAATTCCAAAAGCG', 'G4'), ('GTTATTATGACTTCAT', 'G4'), ('GACCAAAAAGCAGTAT', 'G4'), ('CCCCAGGTATAAAATA', 'G4'), ('CTGAAACCTTGTCCTA', 'G4'), ('GAAAATCGAGCTTTAA', 'G4'), ('CCAATAAAATACGATG', 'G4'), ('ACTGAACAGTATAACT', 'G4'), ('CAAGAAATGTAGTGAA', 'G4'), ('TAGCTGGGCAAAGGCT', 'G4'), ('TATCATTTCATCTACA', 'G4'), ('TCCAAACAGCGTTAAA', 'G4'), ('TCAAACTATGATATTC', 'G4'), ('AAACTTCGTGGTATAC', 'G4'), ('CCGATAAGACGTCGCT', 'G4'), ('CCACGTTCATTAGATG', 'G4'), ('CTATCTTAATCTACAG', 'G4'), ('TCAATGAATGCGGGGT', 'G4'), ('CGCAAGGGATACTAAC', 'G4'), ('TATCGCAATATGATAA', 'G4'), ('CTAAGGGCCTGTTCTT', 'G4'), ('AAGCGGTTTAGGTCCA', 'G4'), ('AATATACCGGCACTAC', 'G4'), ('CTGCGAATATTGTGAC', 'G4'), ('TAGATAATAAGATTCA', 'G4'), ('AGCATGAGCTTGTCAT', 'G4'), ('CAAAAAGCTAATAAGT', 'G4'), ('TCGCGGTAGATTTGCG', 'G4'), ('TAGTTGCCCCGACCTG', 'G4'), ('TCTTGAATTTCATGGA', 'G4'), ('TGGCTAGCGCACACCA', 'G4'), ('ATGGTTATCTTACCTT', 'G4'), ('GGTTGCGTAGTTAATC', 'G4'), ('CGTCAGAAGTTTATAA', 'G4'), ('TCTTAACTACCCGATG', 'G4'), ('AACAATTAATTTTTCA', 'G4'), ('ATCGATTCGATTGACG', 'G4'), ('GACCTCCTGGGCACGC', 'G4'), ('GCTGGTGCACAAGATT', 'G4'), ('CTCATTACAGAAATTG', 'G4'), ('TTATAATGGCCGGTAT', 'G4'), ('CCAGAGACACGCTAGG', 'G4'), ('AATCTTTCCAATCTTG', 'G4'), ('CCTATAAGGCCTTACG', 'G4'), ('GCATTATAATCTTGTG', 'G5'), ('CTGAAACCTTGTCCTA', 'G5'), ('TTAGTCATCTGGGTGC', 'G5'), ('TATTATCTAAACGGCG', 'G5'), ('AAGCTAATCGTAGTCC', 'G5'), ('GCTAATTCCAAAAGCG', 'G5'), ('CAAGAAATGTAGTGAA', 'G5'), ('CCAATAAAATACGATG', 'G5'), ('TAATAACTTGAGATTC', 'G5'), ('AACGACACTTACATCC', 'G5'), ('AATATACCGGCACTAC', 'G5'), ('TCAATGAATGCGGGGT', 'G5'), ('CCACGTTCATTAGATG', 'G5'), ('AAACTTCGTGGTATAC', 'G5'), ('TATCGCAATATGATAA', 'G5'), ('TAGATAATAAGATTCA', 'G5'), ('TCTTGAATTTCATGGA', 'G5'), ('AAGAAATTATGGCAGG', 'G5'), ('CGCAAGGGATACTAAC', 'G5'), ('CTGCGAATATTGTGAC', 'G5'), ('TGGCTAGCGCACACCA', 'G5'), ('AACAATTAATTTTTCA', 'G5'), ('TAGTTGCCCCGACCTG', 'G5'), ('GACCTCCTGGGCACGC', 'G5'), ('CCCGCTAACCCTGTCT', 'G5'), ('GCTAATTCCAAAAGCG', 'G6'), ('CGCAAGGGATACTAAC', 'G6'), ('TCTTAACTACCCGATG', 'G6'), ('TAGTTGCCCCGACCTG', 'G6'), ('AATCTTTCCAATCTTG', 'G6'), ('AATGACAGCTGTCTAG', 'G9')]
Check how many dilutions we have per barcode / serum-replicate:
n_dilutions = (
frac_infectivity.groupby(["serum_replicate", "strain", "barcode"], as_index=False)
.aggregate(**{"number of dilutions": pd.NamedAgg("dilution_factor", "nunique")})
.assign(
fails_qc=lambda x: (
x["number of dilutions"]
< qc_thresholds["min_dilutions_per_barcode_serum_replicate"]
),
)
)
n_dilutions_chart = (
alt.Chart(n_dilutions)
.add_params(barcode_selection)
.encode(
alt.X("number of dilutions", scale=alt.Scale(nice=False, padding=4)),
alt.Y("strain", title=None),
alt.Column(
"serum_replicate",
title=None,
header=alt.Header(labelFontSize=12, labelFontStyle="bold", labelPadding=0),
),
alt.Fill(
"fails_qc",
title=f"fails {qc_thresholds['min_dilutions_per_barcode_serum_replicate']=}",
legend=alt.Legend(titleLimit=500, orient="bottom"),
),
strokeWidth=alt.condition(barcode_selection, alt.value(2), alt.value(0)),
size=alt.condition(barcode_selection, alt.value(55), alt.value(35)),
tooltip=[
alt.Tooltip(c, format=".3g") if n_dilutions[c].dtype == float else c
for c in n_dilutions.columns
],
)
.mark_circle(stroke="black", strokeOpacity=1, fillOpacity=0.45)
.properties(
height=alt.Step(10),
width=120,
title=alt.TitleParams(
"number of dilutions for each barcode for each serum-replicate", dy=-2
),
)
)
display(n_dilutions_chart)
# drop barcode / serum-replicates failing QC
min_dilutions_per_barcode_serum_replicate_drops = list(
n_dilutions.query("fails_qc")[["barcode", "serum_replicate"]].itertuples(
index=False, name=None
)
)
print(
f"\nDropping {len(min_dilutions_per_barcode_serum_replicate_drops)} barcode/serum-replicates for failing "
f"{qc_thresholds['min_dilutions_per_barcode_serum_replicate']=}: "
+ str(min_dilutions_per_barcode_serum_replicate_drops)
)
qc_drops["barcode_serum_replicates"].update(
{
w: "min_dilutions_per_barcode_serum_replicate"
for w in min_dilutions_per_barcode_serum_replicate_drops
}
)
frac_infectivity = frac_infectivity[
~frac_infectivity.assign(
barcode_serum_replicate=lambda x: x.apply(
lambda r: (r["barcode"], r["serum_replicate"]), axis=1
)
)["barcode_serum_replicate"].isin(qc_drops["barcode_serum_replicates"])
]
Dropping 0 barcode/serum-replicates for failing qc_thresholds['min_dilutions_per_barcode_serum_replicate']=6: []
Fit neutralization curves without applying QC to curves¶
First fit curves to all serum replicates, then we will apply QC on the curve fits. Note that the fitting is done to the fraction infectivities with the ceiling:
fits_noqc = neutcurve.CurveFits(
frac_infectivity.rename(
columns={
"frac_infectivity_ceiling": "fraction infectivity",
"concentration": "serum concentration",
}
),
conc_col="serum concentration",
fracinf_col="fraction infectivity",
virus_col="strain",
serum_col="serum_replicate",
replicate_col="barcode",
fixtop=curvefit_params["fixtop"],
fixbottom=curvefit_params["fixbottom"],
fixslope=curvefit_params["fixslope"],
)
Determine which fits fail the curve fitting QC, and plot them. Note the plot indicates as failing QC any barcode / serum-replicate that fails, even if we are also specified to ignore the QC for that one (so it will not be removed later):
goodness_of_fit = curvefit_qc["goodness_of_fit"]
fit_params_noqc = (
frac_infectivity.groupby(["serum_replicate", "barcode"], as_index=False)
.aggregate(max_frac_infectivity=pd.NamedAgg("frac_infectivity_ceiling", "max"))
.merge(
fits_noqc.fitParams(average_only=False, no_average=True)[
["serum", "virus", "replicate", "r2", "rmsd"]
].rename(columns={"serum": "serum_replicate", "replicate": "barcode"}),
validate="one_to_one",
)
.assign(
fails_max_frac_infectivity_at_least=lambda x: (
x["max_frac_infectivity"] < curvefit_qc["max_frac_infectivity_at_least"]
),
fails_goodness_of_fit=lambda x: (
(x["r2"] < goodness_of_fit["min_R2"])
& (x["rmsd"] > goodness_of_fit["max_RMSD"])
),
fails_qc=lambda x: (
x["fails_max_frac_infectivity_at_least"] | x["fails_goodness_of_fit"]
),
ignore_qc=lambda x: x.apply(
lambda r: (
(
r["serum_replicate"]
in curvefit_qc["serum_replicates_ignore_curvefit_qc"]
)
or (
(r["barcode"], r["serum_replicate"])
in curvefit_qc["barcode_serum_replicates_ignore_curvefit_qc"]
)
),
axis=1,
),
)
)
print(f"Plotting barcode / serum-replicates that fail {curvefit_qc=}\n")
for prop, col in [
("max frac infectivity", "max_frac_infectivity"),
("curve fit R2", "r2"),
("curve fit RMSD", "rmsd"),
]:
fit_params_noqc_chart = (
alt.Chart(fit_params_noqc)
.add_params(barcode_selection)
.encode(
alt.X(col, title=prop, scale=alt.Scale(nice=False, padding=4)),
alt.Y("virus", title=None),
alt.Fill("fails_qc"),
alt.Column(
"serum_replicate",
title=None,
header=alt.Header(
labelFontSize=12, labelFontStyle="bold", labelPadding=0
),
),
strokeWidth=alt.condition(barcode_selection, alt.value(2), alt.value(0)),
size=alt.condition(barcode_selection, alt.value(55), alt.value(35)),
tooltip=[
alt.Tooltip(c, format=".3g") if fit_params_noqc[c].dtype == float else c
for c in fit_params_noqc.columns
],
)
.mark_circle(stroke="black", strokeOpacity=1, fillOpacity=0.55)
.properties(
height=alt.Step(10),
width=120,
title=alt.TitleParams(f"{prop} for each barcode serum-replicate", dy=-2),
)
)
display(fit_params_noqc_chart)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: divide by zero encountered in divide return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: divide by zero encountered in divide return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
Plotting barcode / serum-replicates that fail curvefit_qc={'max_frac_infectivity_at_least': 0.0, 'goodness_of_fit': {'min_R2': 0.5, 'max_RMSD': 0.15}, 'serum_replicates_ignore_curvefit_qc': [], 'barcode_serum_replicates_ignore_curvefit_qc': []}
Now get all barcode / serum-replicate pairs that fail any of the QC. Plot curves for just these virus / serum-replicates (we plot all barcodes for a virus even if just one fails QC), and then exclude any that are not specified to ignore the QC:
barcode_serum_replicates_fail_qc = fit_params_noqc.query("fails_qc").reset_index(
drop=True
)
print(f"Here are barcode / serum-replicates that fail {curvefit_qc=}")
display(barcode_serum_replicates_fail_qc)
if len(barcode_serum_replicates_fail_qc):
print("\nCurves for viruses and serum-replicates with at least one failed barcode:")
fig, _ = fits_noqc.plotReplicates(
sera=sorted(barcode_serum_replicates_fail_qc["serum_replicate"].unique()),
viruses=sorted(barcode_serum_replicates_fail_qc["virus"].unique()),
attempt_shared_legend=False,
legendfontsize=8,
titlesize=10,
ticksize=10,
ncol=6,
draw_in_bounds=True,
)
display(fig)
plt.close(fig)
# drop barcode / serum-replicates failing QC
for qc_filter in ["max_frac_infectivity_at_least", "goodness_of_fit"]:
fits_qc_drops = list(
fit_params_noqc.query(f"fails_{qc_filter} and (not ignore_qc)")[
["barcode", "serum_replicate"]
].itertuples(index=False, name=None)
)
print(
f"\nDropping {len(fits_qc_drops)} barcode/serum-replicates for failing "
f"{qc_filter}={curvefit_qc[qc_filter]}: " + str(fits_qc_drops)
)
qc_drops["barcode_serum_replicates"].update({w: qc_filter for w in fits_qc_drops})
frac_infectivity = frac_infectivity[
~frac_infectivity.assign(
barcode_serum_replicate=lambda x: x.apply(
lambda r: (r["barcode"], r["serum_replicate"]), axis=1
)
)["barcode_serum_replicate"].isin(qc_drops["barcode_serum_replicates"])
]
fit_params_noqc = fit_params_noqc[
~fit_params_noqc.assign(
barcode_serum_replicate=lambda x: x.apply(
lambda r: (r["barcode"], r["serum_replicate"]), axis=1
)
)["barcode_serum_replicate"].isin(qc_drops["barcode_serum_replicates"])
]
Here are barcode / serum-replicates that fail curvefit_qc={'max_frac_infectivity_at_least': 0.0, 'goodness_of_fit': {'min_R2': 0.5, 'max_RMSD': 0.15}, 'serum_replicates_ignore_curvefit_qc': [], 'barcode_serum_replicates_ignore_curvefit_qc': []}
| serum_replicate | barcode | max_frac_infectivity | virus | r2 | rmsd | fails_max_frac_infectivity_at_least | fails_goodness_of_fit | fails_qc | ignore_qc | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | A230212d0_r16 | AATGACAGCTGTCTAG | 1.0 | A/Sydney/715/2023 | 0.200480 | 0.207000 | False | True | True | False |
| 1 | A230212d0_r16 | AGGGACTTTATTGTCC | 1.0 | A/South_Dakota/22/2023 | 0.438001 | 0.180657 | False | True | True | False |
| 2 | A230212d28_r16 | AATGACAGCTGTCTAG | 1.0 | A/Sydney/715/2023 | 0.418578 | 0.228428 | False | True | True | False |
Curves for viruses and serum-replicates with at least one failed barcode:
Dropping 0 barcode/serum-replicates for failing max_frac_infectivity_at_least=0.0: []
Dropping 3 barcode/serum-replicates for failing goodness_of_fit={'min_R2': 0.5, 'max_RMSD': 0.15}: [('AATGACAGCTGTCTAG', 'A230212d0_r16'), ('AGGGACTTTATTGTCC', 'A230212d0_r16'), ('AATGACAGCTGTCTAG', 'A230212d28_r16')]
Fit neutralization curves after applying QC¶
No we re-fit curves after applying all the QC:
fits_qc = neutcurve.CurveFits(
frac_infectivity.rename(
columns={
"frac_infectivity_ceiling": "fraction infectivity",
"concentration": "serum concentration",
}
),
conc_col="serum concentration",
fracinf_col="fraction infectivity",
virus_col="strain",
serum_col="serum",
replicate_col="plate_barcode",
fixtop=curvefit_params["fixtop"],
fixbottom=curvefit_params["fixbottom"],
fixslope=curvefit_params["fixslope"],
)
fit_params_qc = fits_qc.fitParams(average_only=False, no_average=True)
assert len(fit_params_qc) <= len(
fits_noqc.fitParams(average_only=False, no_average=True)
)
print(f"Assigning fits for this plate to {group}")
fit_params_qc.insert(0, "group", group)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: divide by zero encountered in divide return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: divide by zero encountered in divide return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s) /fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power return b + (t - b) / (1 + (c / m) ** s)
Assigning fits for this plate to pilot
Plot all the curves that passed QC:
if fits_qc.sera:
_ = fits_qc.plotReplicates(
attempt_shared_legend=False,
legendfontsize=8,
titlesize=10,
ticksize=10,
ncol=6,
draw_in_bounds=True,
)
else:
print("No sera passed QC.")
Save results to files¶
print(f"Writing fraction infectivities to {frac_infectivity_csv}")
(
frac_infectivity[
[
"serum",
"strain",
"plate_barcode",
"dilution_factor",
"frac_infectivity_raw",
"frac_infectivity_ceiling",
]
]
.sort_values(["serum", "plate_barcode", "dilution_factor"])
.to_csv(frac_infectivity_csv, index=False, float_format="%.4g")
)
print(f"\nWriting fit parameters to {fits_csv}")
(
fit_params_qc.drop(columns=["nreplicates", "ic50_str"]).to_csv(
fits_csv, index=False, float_format="%.4g"
)
)
print(f"\nPickling neutcurve.CurveFits object for these data to {fits_pickle}")
with open(fits_pickle, "wb") as f:
pickle.dump(fits_qc, f)
print(f"\nWriting QC drops to {qc_drops_yaml}")
def tup_to_str(x):
return " ".join(x) if isinstance(x, tuple) else x
qc_drops_for_yaml = {
key: {tup_to_str(key2): val2 for key2, val2 in val.items()}
for key, val in qc_drops.items()
}
with open(qc_drops_yaml, "w") as f:
yaml.YAML(typ="rt").dump(qc_drops_for_yaml, f)
print("\nHere are the QC drops:\n***************************")
yaml.YAML(typ="rt").dump(qc_drops_for_yaml, sys.stdout)
Writing fraction infectivities to results/plates/plate2/frac_infectivity.csv
Writing fit parameters to results/plates/plate2/curvefits.csv
Pickling neutcurve.CurveFits object for these data to results/plates/plate2/curvefits.pickle
Writing QC drops to results/plates/plate2/qc_drops.yml
Here are the QC drops:
***************************
wells: {}
barcodes:
AAAGTAGCAGAGGATT: min_neut_standard_frac_per_well
AAATTCACAATATCCA: min_neut_standard_frac_per_well
AGACCATCGCACCCAA: min_neut_standard_frac_per_well
ATAACGTTTGTGCAAA: min_neut_standard_frac_per_well
CAAAAGCAGCACGATA: min_neut_standard_frac_per_well
CATAAAAGACTGTATA: min_neut_standard_frac_per_well
CGTACGTATGTCCCAG: min_neut_standard_frac_per_well
CGTCCCTGGCGTGTCG: min_neut_standard_frac_per_well
CGTTAACGGCCTATCC: min_neut_standard_frac_per_well
TATATGGAATACTAAA: min_neut_standard_frac_per_well
TCTCCGATAGCCCTAC: min_neut_standard_frac_per_well
TGTTGTAATCTGAATA: min_neut_standard_frac_per_well
barcode_wells:
GCTAATTCCAAAAGCG D2: max_frac_infectivity_per_viral_barcode_well
TAGTTGCCCCGACCTG D2: max_frac_infectivity_per_viral_barcode_well
TGATCCGCAAGCTTAG D2: max_frac_infectivity_per_viral_barcode_well
GCTAATTCCAAAAGCG D3: max_frac_infectivity_per_viral_barcode_well
CTGAAACCTTGTCCTA D3: max_frac_infectivity_per_viral_barcode_well
CCAATAAAATACGATG D3: max_frac_infectivity_per_viral_barcode_well
TTAGTCATCTGGGTGC D3: max_frac_infectivity_per_viral_barcode_well
CCCCAGGTATAAAATA D3: max_frac_infectivity_per_viral_barcode_well
ACTGAACAGTATAACT D3: max_frac_infectivity_per_viral_barcode_well
TATCATTTCATCTACA D3: max_frac_infectivity_per_viral_barcode_well
GTTATTATGACTTCAT D3: max_frac_infectivity_per_viral_barcode_well
GAAAATCGAGCTTTAA D3: max_frac_infectivity_per_viral_barcode_well
AAACTTCGTGGTATAC D3: max_frac_infectivity_per_viral_barcode_well
AAAGATAAATTCAAAA D3: max_frac_infectivity_per_viral_barcode_well
AACGACACTTACATCC D3: max_frac_infectivity_per_viral_barcode_well
CTATCTTAATCTACAG D3: max_frac_infectivity_per_viral_barcode_well
TCCAAACAGCGTTAAA D3: max_frac_infectivity_per_viral_barcode_well
AATATACCGGCACTAC D3: max_frac_infectivity_per_viral_barcode_well
TCAATGAATGCGGGGT D3: max_frac_infectivity_per_viral_barcode_well
AGATCATAAGCAATAA D3: max_frac_infectivity_per_viral_barcode_well
CCACGTTCATTAGATG D3: max_frac_infectivity_per_viral_barcode_well
TATCGCAATATGATAA D3: max_frac_infectivity_per_viral_barcode_well
CGCAAGGGATACTAAC D3: max_frac_infectivity_per_viral_barcode_well
CGTCAGAAGTTTATAA D3: max_frac_infectivity_per_viral_barcode_well
TCTTAACTACCCGATG D3: max_frac_infectivity_per_viral_barcode_well
TTATGTTTTAATGGTA D3: max_frac_infectivity_per_viral_barcode_well
AACAATTAATTTTTCA D3: max_frac_infectivity_per_viral_barcode_well
TAGTTGCCCCGACCTG D3: max_frac_infectivity_per_viral_barcode_well
GCTGGTGCACAAGATT D3: max_frac_infectivity_per_viral_barcode_well
TGACAAACACCTGAGG D3: max_frac_infectivity_per_viral_barcode_well
CAGCCGCTAAAATGAT D3: max_frac_infectivity_per_viral_barcode_well
CCGCAATGACAATTTG D3: max_frac_infectivity_per_viral_barcode_well
AATCTTTCCAATCTTG D3: max_frac_infectivity_per_viral_barcode_well
CCAGAGACACGCTAGG D3: max_frac_infectivity_per_viral_barcode_well
CCCGCTAACCCTGTCT D3: max_frac_infectivity_per_viral_barcode_well
GCTAATTCCAAAAGCG D4: max_frac_infectivity_per_viral_barcode_well
CCAATAAAATACGATG D4: max_frac_infectivity_per_viral_barcode_well
CCACGTTCATTAGATG D4: max_frac_infectivity_per_viral_barcode_well
AAACTTCGTGGTATAC D4: max_frac_infectivity_per_viral_barcode_well
TTACGTCAATGTTTGA D4: max_frac_infectivity_per_viral_barcode_well
AACAATTAATTTTTCA D4: max_frac_infectivity_per_viral_barcode_well
CAAAAAGCTAATAAGT D4: max_frac_infectivity_per_viral_barcode_well
TCGCGGTAGATTTGCG D4: max_frac_infectivity_per_viral_barcode_well
TCTTAACTACCCGATG D4: max_frac_infectivity_per_viral_barcode_well
CTCCAATAGGAGACGA D4: max_frac_infectivity_per_viral_barcode_well
AATCTTTCCAATCTTG D4: max_frac_infectivity_per_viral_barcode_well
AACCACCCCAGAGATG D10: max_frac_infectivity_per_viral_barcode_well
GCTAATTCCAAAAGCG G2: max_frac_infectivity_per_viral_barcode_well
GCATTATAATCTTGTG G2: max_frac_infectivity_per_viral_barcode_well
TACCATTTTGGTCCGC G2: max_frac_infectivity_per_viral_barcode_well
CCAATAAAATACGATG G2: max_frac_infectivity_per_viral_barcode_well
TCTTAACTACCCGATG G2: max_frac_infectivity_per_viral_barcode_well
AATCTTTCCAATCTTG G2: max_frac_infectivity_per_viral_barcode_well
TAAAAGAATGATGGTC G3: max_frac_infectivity_per_viral_barcode_well
CTGAAACCTTGTCCTA G3: max_frac_infectivity_per_viral_barcode_well
ATCGAAAAAACTGCAA G3: max_frac_infectivity_per_viral_barcode_well
TGTAGTATAAGAATAA G3: max_frac_infectivity_per_viral_barcode_well
GCATTATAATCTTGTG G3: max_frac_infectivity_per_viral_barcode_well
TATCAATTCGGTATTA G3: max_frac_infectivity_per_viral_barcode_well
TACCATTTTGGTCCGC G3: max_frac_infectivity_per_viral_barcode_well
TTGCTAGTCTACCTGA G3: max_frac_infectivity_per_viral_barcode_well
GCTAATTCCAAAAGCG G3: max_frac_infectivity_per_viral_barcode_well
AGTTATGTAAAACGTG G3: max_frac_infectivity_per_viral_barcode_well
TTAGTCATCTGGGTGC G3: max_frac_infectivity_per_viral_barcode_well
CCAATAAAATACGATG G3: max_frac_infectivity_per_viral_barcode_well
GAAAATCGAGCTTTAA G3: max_frac_infectivity_per_viral_barcode_well
AAAGATAAATTCAAAA G3: max_frac_infectivity_per_viral_barcode_well
GTTATTATGACTTCAT G3: max_frac_infectivity_per_viral_barcode_well
TATCATTTCATCTACA G3: max_frac_infectivity_per_viral_barcode_well
ACGTAAATCCCCACAA G3: max_frac_infectivity_per_viral_barcode_well
CCACAAGTTTGAAAAC G3: max_frac_infectivity_per_viral_barcode_well
TAGCTGGGCAAAGGCT G3: max_frac_infectivity_per_viral_barcode_well
TCAAACTATGATATTC G3: max_frac_infectivity_per_viral_barcode_well
CTATCTTAATCTACAG G3: max_frac_infectivity_per_viral_barcode_well
CAAGAAATGTAGTGAA G3: max_frac_infectivity_per_viral_barcode_well
AAACTTCGTGGTATAC G3: max_frac_infectivity_per_viral_barcode_well
TCCAAACAGCGTTAAA G3: max_frac_infectivity_per_viral_barcode_well
GACAGAAACAAAATTA G3: max_frac_infectivity_per_viral_barcode_well
TGCCGATCCAATTGAT G3: max_frac_infectivity_per_viral_barcode_well
AATATACCGGCACTAC G3: max_frac_infectivity_per_viral_barcode_well
TATCGCAATATGATAA G3: max_frac_infectivity_per_viral_barcode_well
CCATCACCTTATACAC G3: max_frac_infectivity_per_viral_barcode_well
CCACGTTCATTAGATG G3: max_frac_infectivity_per_viral_barcode_well
CAAGACAAGCCCTATA G3: max_frac_infectivity_per_viral_barcode_well
CGCAAGGGATACTAAC G3: max_frac_infectivity_per_viral_barcode_well
TCTTAACTACCCGATG G3: max_frac_infectivity_per_viral_barcode_well
TAGATAATAAGATTCA G3: max_frac_infectivity_per_viral_barcode_well
TGGCTAGCGCACACCA G3: max_frac_infectivity_per_viral_barcode_well
AACAATTAATTTTTCA G3: max_frac_infectivity_per_viral_barcode_well
TCTTGAATTTCATGGA G3: max_frac_infectivity_per_viral_barcode_well
CGTCAGAAGTTTATAA G3: max_frac_infectivity_per_viral_barcode_well
ATGGTTATCTTACCTT G3: max_frac_infectivity_per_viral_barcode_well
AATCTTTCCAATCTTG G3: max_frac_infectivity_per_viral_barcode_well
CATAATGCACAAACGC G3: max_frac_infectivity_per_viral_barcode_well
TAGTTGCCCCGACCTG G3: max_frac_infectivity_per_viral_barcode_well
GCTGGTGCACAAGATT G3: max_frac_infectivity_per_viral_barcode_well
CCGCAATGACAATTTG G3: max_frac_infectivity_per_viral_barcode_well
CAATTCGCCGTTCCCC G3: max_frac_infectivity_per_viral_barcode_well
TAAAAGAATGATGGTC G4: max_frac_infectivity_per_viral_barcode_well
ATCGAAAAAACTGCAA G4: max_frac_infectivity_per_viral_barcode_well
GCATTATAATCTTGTG G4: max_frac_infectivity_per_viral_barcode_well
AGTTATGTAAAACGTG G4: max_frac_infectivity_per_viral_barcode_well
TACCATTTTGGTCCGC G4: max_frac_infectivity_per_viral_barcode_well
TTAGTCATCTGGGTGC G4: max_frac_infectivity_per_viral_barcode_well
TATTATCTAAACGGCG G4: max_frac_infectivity_per_viral_barcode_well
GCTAATTCCAAAAGCG G4: max_frac_infectivity_per_viral_barcode_well
GTTATTATGACTTCAT G4: max_frac_infectivity_per_viral_barcode_well
GACCAAAAAGCAGTAT G4: max_frac_infectivity_per_viral_barcode_well
CCCCAGGTATAAAATA G4: max_frac_infectivity_per_viral_barcode_well
CTGAAACCTTGTCCTA G4: max_frac_infectivity_per_viral_barcode_well
GAAAATCGAGCTTTAA G4: max_frac_infectivity_per_viral_barcode_well
CCAATAAAATACGATG G4: max_frac_infectivity_per_viral_barcode_well
ACTGAACAGTATAACT G4: max_frac_infectivity_per_viral_barcode_well
CAAGAAATGTAGTGAA G4: max_frac_infectivity_per_viral_barcode_well
TAGCTGGGCAAAGGCT G4: max_frac_infectivity_per_viral_barcode_well
TATCATTTCATCTACA G4: max_frac_infectivity_per_viral_barcode_well
TCCAAACAGCGTTAAA G4: max_frac_infectivity_per_viral_barcode_well
TCAAACTATGATATTC G4: max_frac_infectivity_per_viral_barcode_well
AAACTTCGTGGTATAC G4: max_frac_infectivity_per_viral_barcode_well
CCGATAAGACGTCGCT G4: max_frac_infectivity_per_viral_barcode_well
CCACGTTCATTAGATG G4: max_frac_infectivity_per_viral_barcode_well
CTATCTTAATCTACAG G4: max_frac_infectivity_per_viral_barcode_well
TCAATGAATGCGGGGT G4: max_frac_infectivity_per_viral_barcode_well
CGCAAGGGATACTAAC G4: max_frac_infectivity_per_viral_barcode_well
TATCGCAATATGATAA G4: max_frac_infectivity_per_viral_barcode_well
CTAAGGGCCTGTTCTT G4: max_frac_infectivity_per_viral_barcode_well
AAGCGGTTTAGGTCCA G4: max_frac_infectivity_per_viral_barcode_well
AATATACCGGCACTAC G4: max_frac_infectivity_per_viral_barcode_well
CTGCGAATATTGTGAC G4: max_frac_infectivity_per_viral_barcode_well
TAGATAATAAGATTCA G4: max_frac_infectivity_per_viral_barcode_well
AGCATGAGCTTGTCAT G4: max_frac_infectivity_per_viral_barcode_well
CAAAAAGCTAATAAGT G4: max_frac_infectivity_per_viral_barcode_well
TCGCGGTAGATTTGCG G4: max_frac_infectivity_per_viral_barcode_well
TAGTTGCCCCGACCTG G4: max_frac_infectivity_per_viral_barcode_well
TCTTGAATTTCATGGA G4: max_frac_infectivity_per_viral_barcode_well
TGGCTAGCGCACACCA G4: max_frac_infectivity_per_viral_barcode_well
ATGGTTATCTTACCTT G4: max_frac_infectivity_per_viral_barcode_well
GGTTGCGTAGTTAATC G4: max_frac_infectivity_per_viral_barcode_well
CGTCAGAAGTTTATAA G4: max_frac_infectivity_per_viral_barcode_well
TCTTAACTACCCGATG G4: max_frac_infectivity_per_viral_barcode_well
AACAATTAATTTTTCA G4: max_frac_infectivity_per_viral_barcode_well
ATCGATTCGATTGACG G4: max_frac_infectivity_per_viral_barcode_well
GACCTCCTGGGCACGC G4: max_frac_infectivity_per_viral_barcode_well
GCTGGTGCACAAGATT G4: max_frac_infectivity_per_viral_barcode_well
CTCATTACAGAAATTG G4: max_frac_infectivity_per_viral_barcode_well
TTATAATGGCCGGTAT G4: max_frac_infectivity_per_viral_barcode_well
CCAGAGACACGCTAGG G4: max_frac_infectivity_per_viral_barcode_well
AATCTTTCCAATCTTG G4: max_frac_infectivity_per_viral_barcode_well
CCTATAAGGCCTTACG G4: max_frac_infectivity_per_viral_barcode_well
GCATTATAATCTTGTG G5: max_frac_infectivity_per_viral_barcode_well
CTGAAACCTTGTCCTA G5: max_frac_infectivity_per_viral_barcode_well
TTAGTCATCTGGGTGC G5: max_frac_infectivity_per_viral_barcode_well
TATTATCTAAACGGCG G5: max_frac_infectivity_per_viral_barcode_well
AAGCTAATCGTAGTCC G5: max_frac_infectivity_per_viral_barcode_well
GCTAATTCCAAAAGCG G5: max_frac_infectivity_per_viral_barcode_well
CAAGAAATGTAGTGAA G5: max_frac_infectivity_per_viral_barcode_well
CCAATAAAATACGATG G5: max_frac_infectivity_per_viral_barcode_well
TAATAACTTGAGATTC G5: max_frac_infectivity_per_viral_barcode_well
AACGACACTTACATCC G5: max_frac_infectivity_per_viral_barcode_well
AATATACCGGCACTAC G5: max_frac_infectivity_per_viral_barcode_well
TCAATGAATGCGGGGT G5: max_frac_infectivity_per_viral_barcode_well
CCACGTTCATTAGATG G5: max_frac_infectivity_per_viral_barcode_well
AAACTTCGTGGTATAC G5: max_frac_infectivity_per_viral_barcode_well
TATCGCAATATGATAA G5: max_frac_infectivity_per_viral_barcode_well
TAGATAATAAGATTCA G5: max_frac_infectivity_per_viral_barcode_well
TCTTGAATTTCATGGA G5: max_frac_infectivity_per_viral_barcode_well
AAGAAATTATGGCAGG G5: max_frac_infectivity_per_viral_barcode_well
CGCAAGGGATACTAAC G5: max_frac_infectivity_per_viral_barcode_well
CTGCGAATATTGTGAC G5: max_frac_infectivity_per_viral_barcode_well
TGGCTAGCGCACACCA G5: max_frac_infectivity_per_viral_barcode_well
AACAATTAATTTTTCA G5: max_frac_infectivity_per_viral_barcode_well
TAGTTGCCCCGACCTG G5:
max_frac_infectivity_per_viral_barcode_well
GACCTCCTGGGCACGC G5: max_frac_infectivity_per_viral_barcode_well
CCCGCTAACCCTGTCT G5: max_frac_infectivity_per_viral_barcode_well
GCTAATTCCAAAAGCG G6: max_frac_infectivity_per_viral_barcode_well
CGCAAGGGATACTAAC G6: max_frac_infectivity_per_viral_barcode_well
TCTTAACTACCCGATG G6: max_frac_infectivity_per_viral_barcode_well
TAGTTGCCCCGACCTG G6: max_frac_infectivity_per_viral_barcode_well
AATCTTTCCAATCTTG G6: max_frac_infectivity_per_viral_barcode_well
AATGACAGCTGTCTAG G9: max_frac_infectivity_per_viral_barcode_well
barcode_serum_replicates:
AATGACAGCTGTCTAG A230212d0_r16: goodness_of_fit
AGGGACTTTATTGTCC A230212d0_r16: goodness_of_fit
AATGACAGCTGTCTAG A230212d28_r16: goodness_of_fit
serum_replicates: {}